The Lipid Formulation Classification System: characteristic features, pros and cons of the four essential types of ‘lipid’ formulations.
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More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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\r\n\tTo understand the brain means to be able to reliably manipulate it and predict its response. Neuroscientists have long used electrophysiological techniques to stimulate particular brain areas or even single neurons. Electrical stimuli activate neural circuitry, often without being able to stop the neuronal activity. Neuropharmacological tools are based on drugs that are slow in their effects or not specific enough to stimulate individual cells. In 2005, a set of new techniques started to emerge that combined optical stimuli with genetic tools in order to control events in individual cells. The field of optogenetics has since revolutionized experimental approaches to study cell signaling, metabolism, brain circuits, and organismal behavior. Genetic tools allow the insertion of genes into cells that afterward respond to specific wavelengths of light. Subsequently, light can turn on or off specific signal cascades in cells and even trigger or inhibit the behavior of organisms.
\r\n\r\n\tThis book will illustrate optogenetics in all its dimensions, from historical perspectives to technological advances, as well as the use of optogenetic tools in health and disease, in various animal models and organisms. Targeted at students and researchers in biological, engineering, medical, and related disciplines, this book will provide an overview of the current work that is being done in this exciting, new field and will highlight any gaps and areas of optogenetics that would benefit from further exploration.
",isbn:"978-1-83969-377-9",printIsbn:"978-1-83969-376-2",pdfIsbn:"978-1-83969-378-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"3ae7e24d8f03ff3932bceee4b8d3e727",bookSignature:"Dr. Thomas Heinbockel",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/7921.jpg",keywords:"Monitoring Intracellular Chloride, Optogenetics in Insects, Genetically Encoded Calcium Indicators, Optogenetic Reporters, Channelrhodopsin, Optogenetics in Biomedical Engineering, Optogenetics in Visual Impairments, Light-Induced Neuron and Glia Cell Function, Neuronal Circuit Activation With Optogenetics, Silencing of Cellular Activity, Optical Control of Cell-Cell Interactions, Light Control Proteins",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 20th 2020",dateEndSecondStepPublish:"December 18th 2020",dateEndThirdStepPublish:"February 16th 2021",dateEndFourthStepPublish:"May 7th 2021",dateEndFifthStepPublish:"July 6th 2021",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Internationally trained and experienced researcher of novel areas in neuroscience, affiliated with Howard University College of Medicine, seasoned educator of health sciences students, with a strong background as a journal reviewer and book editor.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"70569",title:"Dr.",name:"Thomas",middleName:null,surname:"Heinbockel",slug:"thomas-heinbockel",fullName:"Thomas Heinbockel",profilePictureURL:"https://mts.intechopen.com/storage/users/70569/images/system/70569.jfif",biography:"Thomas Heinbockel, Ph.D., is a Professor and Interim Chair in the Department of Anatomy, Howard University College of Medicine, Washington, DC. Dr. Heinbockel’s laboratory engages in multidisciplinary research to elucidate organizational principles of neural systems in the brain, specifically the limbic and olfactory system. His research has been directed at understanding brain mechanisms of information processing and their relation to neurological and neuropsychiatric disorders. His laboratory also works on translational projects, specifically, the development of novel anti-epileptic drugs and pharmacotherapeutic treatment options for drug addiction. His laboratory analyzes drug actions at the epi- and genetic level using next-generation sequencing technology. The goal of his studies is to conduct innovative basic and applied research on fundamental biological mechanisms involved in disease conditions (Covid-19, HIV). Dr. Heinbockel studied biology at the Philipps-University, Marburg, Germany. His studies of the brain started during his M.S. thesis work at the Max-Planck-Institute for Behavioral Physiology, Starnberg/Seewiesen, Germany. Subsequently, he completed a Ph.D. in Neuroscience at the University of Arizona, Tucson, Arizona, USA. After graduating, he worked as a Research Associate at the Institute of Physiology, Otto-von-Guericke-University School of Medicine, Magdeburg, Germany. Prior to his arrival at Howard University, Dr. Heinbockel held joint research faculty appointments in the Department of Anatomy & Neurobiology and the Department of Physiology at the University of Maryland School of Medicine, Baltimore, Maryland, USA. He still maintains an adjunct appointment in these departments.",institutionString:"Howard University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"10",totalChapterViews:"0",totalEditedBooks:"7",institution:{name:"Howard University",institutionURL:null,country:{name:"United States of America"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"6",title:"Biochemistry, Genetics and Molecular Biology",slug:"biochemistry-genetics-and-molecular-biology"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"252211",firstName:"Sara",lastName:"Debeuc",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/252211/images/7239_n.png",email:"sara.d@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"3846",title:"Neurochemistry",subtitle:null,isOpenForSubmission:!1,hash:"671f065e6c1035adb042edc442626b8a",slug:"neurochemistry",bookSignature:"Thomas Heinbockel",coverURL:"https://cdn.intechopen.com/books/images_new/3846.jpg",editedByType:"Edited by",editors:[{id:"70569",title:"Dr.",name:"Thomas",surname:"Heinbockel",slug:"thomas-heinbockel",fullName:"Thomas Heinbockel"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1657",title:"Neuroscience",subtitle:null,isOpenForSubmission:!1,hash:"e9a76a5d4740bdeefa66bb4cd6162964",slug:"neuroscience",bookSignature:"Thomas Heinbockel",coverURL:"https://cdn.intechopen.com/books/images_new/1657.jpg",editedByType:"Edited by",editors:[{id:"70569",title:"Dr.",name:"Thomas",surname:"Heinbockel",slug:"thomas-heinbockel",fullName:"Thomas Heinbockel"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7329",title:"Histology",subtitle:null,isOpenForSubmission:!1,hash:"9af2e2fd8f28c4d1b8b9510c3d73e1ec",slug:"histology",bookSignature:"Thomas Heinbockel and Vonnie D.C. 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Csoka",coverURL:"https://cdn.intechopen.com/books/images_new/7480.jpg",editedByType:"Edited by",editors:[{id:"70569",title:"Dr.",name:"Thomas",surname:"Heinbockel",slug:"thomas-heinbockel",fullName:"Thomas Heinbockel"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6694",title:"New Trends in Ion Exchange Studies",subtitle:null,isOpenForSubmission:!1,hash:"3de8c8b090fd8faa7c11ec5b387c486a",slug:"new-trends-in-ion-exchange-studies",bookSignature:"Selcan Karakuş",coverURL:"https://cdn.intechopen.com/books/images_new/6694.jpg",editedByType:"Edited by",editors:[{id:"206110",title:"Dr.",name:"Selcan",surname:"Karakuş",slug:"selcan-karakus",fullName:"Selcan Karakuş"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"69770",title:"Self-Microemulsifying System",doi:"10.5772/intechopen.88603",slug:"self-microemulsifying-system",body:'\nAn advance in in-vitro screening methods such as conjunctional chemistry is leading to publicizing of many potential chemical components with high therapeutic activity. Such rapid identification of highly potent pharmaceutical lead compounds has optimized pharmacodynamic properties but sub-optimal biopharmaceutical characteristics [1]. Most of the drugs are lipophilic in nature and has poor water solubility. Such low water solubility becomes the major challenge in successful development of their oral formulation. Also several drug compounds has low oral bioavailability which further enhances the challenge for the formulator scientist [2, 3]. More than 40% of drugs are lipophilic in nature with poor water solubility. To resolve such challenges, many approaches have been reported to improve the solubility and enhance the oral bioavailability which includes the formation of cyclodextrin complex, lipid based drug delivery system, solid dispersions, micronization, etc. [4, 5]. Among these methods, self-emulsifying systems is one of the most optimistic approaches to enhance the oral bioavailability of poorly water-soluble drugs since it maintains the drug in a solubilized state in the gastrointestinal tract [6]. A stable self-micro emulsifying system consists of mixture of drug, oil, surfactant and co-surfactant. Upon dilution with water it results into fine oil-in-water emulsion with a droplets diameter less than 50 nm [7, 8]. The micro-emulsion droplet of self-micro emulsifying systems entraps the drug molecule completely with 100% efficacy, thus self-micro emulsifying systems shows high potential to deliver low water soluble drug [9]. Rapid emulsion formation helps to keep the drug in a dissolved form and small droplet size offers a considerably larger interfacial surface area which further accelerates the absorption rate of drug with limited solubility. Moreover, the droplets can be rapidly dispersed in blood as well as lymph and the lymphatic drug transport can avoid the first-pass effect [10]. This feature makes self-micro emulsifying systems a significant choice for oral delivery of lipophilic, low bioavailable drugs having ample of lipid solubility [11, 12, 13, 14]. Self-emulsifying systems is a broad term which produces emulsions with a droplet size ranging from a few nanometers to several microns. A self-micro emulsifying system indicates the formulations forming transparent micro-emulsions with oil droplets ranging between 100 and 250 nm. Term self-nano emulsifying system is used to characterize the system which results into emulsion with globule size less than 100 nm [15, 16].
\nLipidic formulations are classified as Type I, II, III, and IV based upon excipients used. Type I formulations are non-self-emulsifying whereas Type II, III, and IV formulations are self-emulsifying. Type of emulsion formed after dilution of self-emulsifying system with water, depends upon the excipients used in formulation. Digestibility of lipidic compositions is also affected by these ingredients. Elements of lipidic systems are represented in the proceeding portion [17, 18]. Classification system of lipid formulation is shown in Table 1.
\nFormulation | \nExcipients | \nProperties | \nPros | \nCons | \n
---|---|---|---|---|
Type I | \nOils lacking of surfactants (e.g. tri-,di-and mono glycerides) | \nNot dispersing, it needs digestion. | \nSimple, Compatibility is excellent for capsule. | \nFormulation has poor solvent capacity unless drug is highly lipophilic. | \n
Type II | \nOils and water-insoluble surfactants. | \nSES formed without water-soluble components | \nUnlikely to lose solvent capacity on dispersion | \nTurbid o/w dispersion (particlesize0.25–2 mm) | \n
Type III | \nOils, surfactants and co solvents (both water insoluble and water-soluble excipients) | \nSES/SMES formed with water-soluble components | \nclear or almost clear dispersion; drug absorption without digestion | \nPossible loss of solvent capacity on dispersion; less easily digested | \n
Type IV | \nWater-soluble surfactants and Co solvents(no oils) | \nFormulation disperses typically to forma micellar solution | \nFormulation has good solvent capacity for many drugs | \nLikely loss of solvent capacity On dispersion; might not be digestible | \n
The Lipid Formulation Classification System: characteristic features, pros and cons of the four essential types of ‘lipid’ formulations.
Type I: Drug with tri-, di- or monoglyceride in lipid based compositions is called as type I formulations. Dilution of type I formulations with aqueous media creates coarse dispersion and is not readily dispersible. Initial digestibility by pancreatic lipase/co-lipase to engender more amphiphilic species is a pivotal necessity for their oral absorption. For potent drugs or drugs with high oil solubility, Type I formulations are preferable.
\nType II: These formulation contain drug with oil and water insoluble surfactants (Hydrophilic lipophilic balance <12), and are also called as self-emulsifying systems. Self-emulsification is mainly acquired at the surfactant concentration above 25% w/w. Surfactant greater than 60% w/w that is at higher Concentration, there is formation of liquid crystalline gel phases at the o/w interface because emulsification is impeded. Such systems generate droplets size above 300 nm, when dispersed in water it developed emulsion which is opaque in nature.
\nType III: Type III formulations consist of drug, oil, surfactants, and co-solvents for both water-soluble and water insoluble. Ethanol, polyethylene glycol and propylene glycols are selected as co-solvents. Such systems generate droplets size below 300 nm, when dispersed in water and are called as self-micro emulsifying systems. The obtained emulsion is either optically clear or somewhat opalescent dispersion.
\nType IV: Type IV formulations consist of drug, water soluble surfactants, and co-solvents. Oil is absent in this type of formulation.
\nDrugs that belong to the Class II and Class IV of biopharmaceutical classification system offer potential platform to enhance the oral bioavailability. Log P of the drug indicates the potential utility of lipid based formulation. Maintenance of drug solubility in gastrointestinal tract is the foremost challenges to oral formulation and especially the increased drug solubility at the absorption site of the gut [19]. Lipophilic drug composite that manifest dissolution rate limited absorption, self-emulsifying systems can provide an improvement in absorption in terms of rate and extent, that results in consistent blood time profiles [7, 20]. Problem of poor solubility and low bioavailability of drug across all categories of biopharmaceutical classification system can be resolved by formulating into self-emulsifying system, as shown in Table 2 [21].
\nBCS class | \nHurdles overcome by SES | \n
---|---|
Class I | \nGut wall efflux, Enzymatic degradation. | \n
Class II | \nSolubility and bioavailability. | \n
Class III | \nEnzymatic degradation, bioavailability and gut wall efflux. | \n
Class IV | \nSolubility, bioavailability, Enzymatic degradation, gut wall efflux. | \n
SES as a solution to various problems to different classes of drugs.
For an oral absorption Lipinski’s rule of five has been widely proposed as a qualitative predictive model. In the discovery setting, the ‘rule of five’ predicts that if there are more than five H– bond donors, it shows poor absorption or poor permeation [22].
\nWhether solubility and log P are sufficient to identify probable drug candidates for such formulations that question arises and also it is noted that biopharmaceutical classification system and Lipinski’s rule of five classification system are useful, particularly at inceptive screening stage, they have some constraint. For recognize the suitable lipid based formulation approach aqueous solubility and log P alone are improbable enough because they do not adequately predict potential in- vivo effects.
\nSelf-emulsifying formulation produces dispersion in gastrointestinal tract by using different excipients. Isotropic mixtures of oils, surfactants, solvents, and co-solvents/surfactants comprise self-emulsifying formulation and it emulsifies in gastrointestinal tract under a gentle agitation [23].
\nDepending upon the type of dispersion produced after dilution with water phase, self-emulsifying formulations are further classified as self-emulsifying systems, self-micro emulsifying systems and self-nano emulsifying systems. Emulsion which is slightly hazy, opalescent or opaque colloidal coarse dispersion is called as self-emulsifying systems. Micro-emulsion which is clear or pellucid, slightly hazy, opalescent, non-opaque colloidal dispersion with droplet size below 150 nm are called as self-micro emulsifying systems. Nano-emulsion which clear or pellucid, slightly hazy, opalescent, non-opaque or substantially non-opaque colloidal dispersion with droplet size below 20 nm in diameter called as self-nano emulsifying systems [24]. For the formulation, excipient should be chosen from the list of generally regarded as safe “GRAS” excipients published by USFDA or from other inactive ingredients approved and published by regulatory agencies.
\nActive Pharmaceutical Ingredient should be soluble in oil phase as this have an impact on the self-micro emulsifying systems to maintain the active pharmaceutical ingredient solubility. Drugs with the low solubility in aqueous media or lipids are strenuous to convey through self-micro emulsifying systems. Exceedingly good solubility in one of the components of self-micro emulsifying systems is require preferably oil phase, if very high dose of drug liked to be administered. For self-micro emulsifying systems, high melting point of drug with log P value around 2 is not appropriate and for self-micro emulsifying systems, lipophilic drugs with the log P values more than 5 are good candidate [19, 25].
\nIn self-emulsifying formulations, oil represents the most important constituent as it solubilizes prominent amounts of the lipophilic drug. Oil promotes self-emulsification and extends the fragment of lipophilic drug transported through the intestinal lymphatic system. Absorption of lipophilic drug from the gastrointestinal tract is enhanced depending upon the molecular nature of the triglyceride used in formulation [26, 27]. Regardless of the noteworthy potential that these lipid excipients have, very few of lipid based formulations has reached to the pharmaceutical market. This may be due to the insufficient data concerning the relatively composite physical chemistry of lipids and scrutinize about formulated drug chemical and physical stability. Incorporation to these studies, its impact on drug absorption is also essential and which depends on interaction of a lipid-based formulation with the gastrointestinal tract environment [28]. Natural edible oils, comprising medium-chain triglycerides, are not commonly preferred in this regard owing to their poor ability to dissolve large amounts of lipophilic drugs [29]. For designing of self-emulsifying systems, varying degrees of saturated and hydrolyzed long and medium chain triglycerides are used. These semi synthetic derivatives form good emulsification systems when used with a large number of solubility enhancing surfactants approved for oral administration. There is polarity deference between the long chain triglyceride and medium-chain triglyceride, a wide micro-emulsion area has been achieved in phase diagram if medium chain triglyceride is used. More is hydrophobic long chain triglyceride, more difficult it becomes to emulsify.
\nThe self-emulsifying system demand incorporation of comparatively large amounts of surfactant in addition to the oil, to convey drug in the formulation. Permeability of the intestinal membrane and affinity between lipids and intestinal membrane will be improved due to effect of surfactant. Surfactants improve the permeability by partitioning into the cell membrane and disrupting the structural organization of the lipid bilayer dominates to permeation enhancement [30]. The two major affairs that command the selection of a surfactant enclose first safety and second hydrophilic lipophilic balance. To formulate self-emulsifying systems, Hydrophilic lipophilic balance of surfactant provides important information. High emulsifying performance is achieved if the emulsifier used in formulation of self-emulsifying systems has high hydrophilicity and hydrophilic lipophilic balance. Therefore, for effective absorption at the site, drug is present in solubilized form for a longer period of time and prevents precipitation of drug substance in gastrointestinal tract lumen [31]. Generally single alkyl chains are more penetrative, so surfactants such as polysorbates and triglyceride ethoxylates are found to be less toxic. Usually the surfactant concentration ranges between 30 and 60% of the total formulation in order to form stable self-micro emulsifying systems [32].
\nStress of interface is decrease in the presence of co-surfactant and it allows the interfacial film sufficient flexibility to take up different curvatures required to form self-micro emulsifying systems over a wide range of composition [33]. The mixture with higher surfactant and co-surfactant: oil ratio assists the formation of self-micro emulsifying systems. Disadvantage of alcohol and other volatile co-solvents is that they get evaporated through the shell of soft or hard gelatin capsules and results into precipitation of drug (Table 3) [34, 35].
\nOils | \nSurfactants | \nCo-surfactants/co-solvent | \n
---|---|---|
Cotton seed oil | \nPolysorbate 20 (Tween 20) | \nSpan 20 | \n
Soybean oil | \nPolysorbate 80 (Tween 80) | \nSpan 80 | \n
Corn oil | \nPolyoxy 35 castor oil (Cremophor RH40) | \nCapryol 90 | \n
Sunflower oil | \nPolyethylene glycol | \n|
Castor oil | \nD-alpha Tocopheryl polyethylene glycol 1000 succinate (TPGS) | \nEthanol | \n
Peanut oil | \nLauroglycol | \n|
Sesame oil | \nIsopropyl alcohol | \n
Example of Oil, Surfactant and Co-surfactant/Co-solvent.
The mechanism by which self-emulsification occurs is not yet well understood. The entropy change of dispersion is greater than the energy required to increase the surface area of the dispersion at that time self-emulsification is occurring. In a conventional emulsion formulation, a free energy is an energy that required developing a new surface between the two phases i.e. oil and water and it can be narrated by
\nwhere G is free energy, N is the droplets number, r is globules radius, and σ is the interfacial energy [22, 26]. The oil and water phase of the emulsion separates upon reduction in the interfacial area and free energy of the system. Conventional emulsifying agent stabilizes the emulsion by forming a monolayer around the emulsion droplets and reduces the interfacial energy, thereby provides a barrier to coalescence. For the formulation self-emulsifying systems free energy requires is either very low or positive or negative then, the emulsion process occurs irrepressible. Very low energy requires for emulsification, it involves destabilization through diminution of interfacial regions. It is necessary to not have any resistance to the surface shearing of the interfacial structure to occur the emulsification. Through the emulsification water penetrates into the various liquid crystals or phases. As soon as binary mixture of oil/non-ionic surfactant comes in contact with aqueous phase, formation of interface between the oil and aqueous phases occurs. Aqueous phase penetrates through this interface and starts solubilizing with oil phase till the limit of solubilization is reached at the interface. There is relationship between the emulsification properties of the surfactant and phase inversion behavior of the system.
\nUpon mild agitation of self-micro emulsifying systems, water penetration occurs quickly and leads to the interference of interface and droplets will be formed as micro-emulsions are thermodynamically stable; equilibrium exists within the system although there is continuous exchange of matter between the different phases [36]. Interchanging of matter usually occurs in two different ways like amalgamation of small droplets followed by the parting of larger droplet into small droplets and fragmentation of droplets which later coagulate with other droplets [37].
\nSelf-emulsifying drug delivery system also poses accountability in contempt of its many assets namely
Drug chemical instability
Large amount of surfactant used in formulation causes irritancy in gastrointestinal tract
Precipitation of lipophilic drugs take place when volatile co-solvent is incorporated [38].
Formulation of self-micro emulsifying systems involves the following steps.
Screening of excipients.
Establishment of pseudoternary phase diagram.
Development of self-micro emulsifying systems.
Characterization of self-micro emulsifying systems.
Selection of the most satisfactory excipients that can be used in the preparation of self-micro emulsifying systems depends on the solubility studies. Solubility of the drug is tested in various oils, surfactants, and co-surfactants [39]. Shake flask method is generally used to performed these type of studies. In these studies excess amount of drug is added to the excipient and then flask is shaken for 48 hours in water bath shaker at room temperature. After 48 hours samples are subjected to centrifugation, then filtered through 0.45 μm filters and drug content is examined [40, 41]. The objective of these solubility studies is to choose oil, surfactant, and co-surfactant that show maximum solubility to the drug. Another objective is accomplishment of optimal drug loading with minimized entire volume of the formulation [42].
\nTo check the emulsification ability, screening of surfactant and co-surfactant is done by mixing known amount of surfactants with equal portion of selected oil and surfactant, and homogenized. The idea about ease of emulsification is obtained when the mixture is added to double distilled water and the number of flask inversions required to form homogenous emulsion is noted [43]. Then, the obtained dilution is tested for turbidity, percentage transmittance and clarity. The surfactant that shows high percentage transmittance at lower flask inversions with high emulsification efficiency is generally selected. Similarly, co-surfactants representing higher emulsification efficiency are selected for self-emulsifying formulation [44].
\nMicro-emulsion is formed by the spontaneous emulsification method and can be depicted with the help of phase diagrams. Construction of phase diagram is a useful approach to study the complex series of interactions that can occur when different components are mixed ternary phase diagram is used to study the phase behavior of three components. Ternary phase diagram represents the system with three components oil, water, and surfactant. But in case of self-micro emulsifying systems, the additional component like co-surfactant/co-solvent addition is most common. Ternary diagram contains three corners that correspond to the 100% of the particular component. In case of addition of fourth component, the ternary diagram can be termed as pseudoternary phase diagram [45]. For building of pseudoternary phase diagram, components of micro-emulsion are examined for emulsification efficiency at various compositions. Emulsions, micro-emulsions, micelles, inverted micelle structures may be form and the degree of formation of these structures can be determined with the formation of ternary phase diagram [46, 47]. The fixed ratio is typically formed by the fusion of surfactant and co-surfactant and it may be the mixture of oil and surfactant. This is mixed with the specific volume of the third phase like oil or co-surfactant; then the other component i.e. water is added in a gradual amounts and with every addition the solution is tested for the clarity, dispersibility, time for self-emulsification, and flowability. The total concentration of all components in each mixture is 100%. In pseudoternary phase diagram, the samples which formed clear solution is denoted by suitable symbols in the phase diagram. The area that is formed when these points are joined indicates the mono-physic micro-emulsion existing area and wide area indicates the good emulsification efficiency [48, 49].
\nThe following points may be useful to read and to understand ternary diagram in an easy way. The three corners of the typical ternary diagram represent three components, that is, A, B, and C. The arrow towards BA indicates increase in proportion of A from 0% concentration (at point B) to 100% concentration (at point A), the arrow towards AC indicates the increase in proportion of C from 0% concentration (at point A) to 100% concentration (at point C), and similarly the arrow towards CB indicates the increase in proportion of B from 0% concentration (at point C) to 100% concentration (at point B). It shows in Figure 1, composition at point O can be known by the following procedure [50].
A line is drawn parallel to CB from point O towards AB. The point where this line intersects with AB indicates the percent composition of A at point O (X).
Then, percent composition of B at point O can be known by drawing a line that is parallel to AC towards BC. The point where this line intersects with BC indicates the percent composition of B at point O (Y).
Similarly, the percent composition of C, at point O can be known by drawing a line that is parallel to AB towards AC (Z).
Typical ternary diagram indicating the composition of A, B, and C at point O.
Self-micro emulsifying systems is prepared by adding drug into the mixture of oil, surfactant, and co-surfactant and then vortexed. In some methods, first drug is dissolved in one of the excipients and later on other excipients are added to this prepared solution. Then, the solution is appropriately mixed and turbidity measured. After 48 hours at climatic condition, the solution is heated if required for the development of clear solution [51, 52].
\nThe assessment of self-emulsification is possible by visual evaluation. After dilution of self-micro emulsifying systems with water, the opaque and milky white appearance indicates the formation of macro emulsion whereas the clear, isotropic, transparent solution indicates the formation of micro-emulsion [53, 54]. Precipitation of drug in diluted self-micro emulsifying systems is evaluated by visual inspection. The stable formulation is obtained when drug precipitation is not noticeable. If the formulation contains water soluble co-solvents then precipitation is common outcome and it can be avoided by enhancing the concentration of surfactant [55, 56].
\nThis is a crucial factor in self-emulsification performance because it determines the rate and extent of drug release, as well as the stability of the emulsion [57]. The droplet size is mainly dependent on the nature and concentration of surfactant. Photon correlation spectroscopy, microscopic techniques or a coulter nanosizer are mainly used for the determination of the emulsion droplet size [58, 59].
\nThis recognizes efficient self-emulsification by determine the dispersion reaching equilibrium quickly in a consistent time [60]. Orbeco-Helle turbidity meter is most commonly used for turbidity measurements. This turbidity meter is connected to dissolution equipment and emulsification time, optical clarity of nano or micro-emulsion formed is recorded after every 15 second. Turbidity can also be discovered in expression of spectroscopic characterization of optical clarity [61].
\nThis is used to identify the charge of the droplets. In conventional self-micro emulsifying systems, the charge on an oil droplet surface is negative because of the presence of free fatty acids. Zeta potential is generally measured by zeta potential analyzer or zeta meter system [11]. Value of zeta potential indicates the stability of emulsion after appropriate dilution. Higher zeta potential indicates the good stability of formulation [62, 63].
\nViscosity of diluted self-micro emulsifying systems formulation is determined by rheometers like brookfield, cone and plate rheometers fitted with cone spindle or rotating spindle brookfield viscometer. During titration, the initial increase in viscosity with subsequent decrease with the increase in water volume attributes to water percolation threshold. This indicates the formation of o/w micro-emulsion from w/o micro-emulsion with intermediate bi-continuous phase [64]. Micro-emulsion can be determined by the graph plotted between shear stress and shear rate. The Newtonian behavior indicates the presence of droplets of small and spherical shape.
\nEfficiency of emulsification of various compositions of medium chain triglyceride systems is determined by using a rotating paddle to assist emulsification in a crude nephelometer [65]. This empowers an assessment of the time taken for emulsification.
\nCloud point is generally determined by gradually increasing the temperature of water bath in which the formulation is placed and measured spectrophotometrically. The point where percentage transmittance decreases signifies the cloud point that is the temperature above which the transparent solution changes to cloudy solution. As the body temperature is 37°C, formulations should exhibit the cloud point more than body temperature to retain its self-emulsification property. Phase separation and decrease in drug solubilization are commonly observed at higher temperature than the cloud point due to the susceptibility of surfactant to dehydration. Cloud point is influenced by drug lipophilicity and other formulation components [66].
\nTransmission electron microscope is used to characterize the sample. In this sample is taken on copper grid. Filter paper is used to form the thin liquid film on the grid. The grid is extinguished in liquid ethane at −180°C and transferred to liquid nitrogen at −196°C [67, 68].
\nThis test gives the indication of transparency of diluted self-micro emulsifying systems formulation. It is determined spectrophotometrically after dilution of formulation with water, keeping water as blank. The percentage transmittance value near to 100% indicates clear and transparent micro-emulsion formation [69].
\nSize and shape of the droplets is determined using small angle neutron scattering. Small-angle neutron scattering experiments use the interference effect of wave lets scattered from different materials in a sample with the different scattering length densities.
\nThese studies are useful to evaluate the consequence of temperature change on formulation. Formulation is diluted with aqueous phase and subjected to centrifugation at 15,000 rpm for 15 min or at 3500 rpm for 30 min. The samples in which the phase separation is not observed further subjected to freeze thaw cycles (−20 and 40°C temperature, respectively) and observed visually. The thermodynamically stable formulations does not show any changes in visual description [70, 71].
\nDrugs with a very high dose are not acceptable for self-micro emulsifying systems unless they exhibit very good solubility in one of the excipients of self-micro emulsifying systems, mostly in a lipophilic phase. The drugs having a little solubility in water and lipids (log P values of approximately 2) are very difficult to deliver by self-micro emulsifying systems.
\nSolubility of the drug in oil phase is important parameter in self-micro emulsifying systems formulation to maintain the drug solubility. A chance of precipitation is probably higher if contribution of surfactant and co-surfactant is greater in formulation. Dilution of self-micro emulsifying systems will owe to decrease solvent capacity of the surfactant or co-surfactant. An equilibrium solubility measurement is carried out to predict the potential cases of precipitation in the gut region.
\nDrug release from the self-micro emulsifying systems is mainly affected by the polarity of the lipid phase. The polarity of the droplet is governed by the hydrophilic lipophilic balance, the chain length and degree of unsaturation of the fatty acid, the molecular weight of the hydrophilic portion and the concentration of the emulsifier. Affinity of drug towards solvent is indicated by polarity. Rapid release of the drug in the aqueous phase is high if the polarity is high.
\n\n
Self-micro emulsifying systems have the same advantage as emulsions, of facilitating the solubility of hydrophobic drugs. Macro-emulsions undergo creaming over a period of time, whereas self-micro emulsifying systems being thermodynamically stable can be stored easily [22].
Most of the self-micro emulsifying systems formulations are in capsule or tablet dosage forms, thus occupying smaller volume, easy to administer and hence improved patient compliance [72].
Self-micro emulsifying systems are advantageous over self-emulsifying systems as the former is less dependent on bile salts for the formation of droplets [73].
Drugs which have propensity to be degraded by the chemical and enzymatic means in gastrointestinal tract can be protected by the formulation of self-micro emulsifying systems as the drug will be presented to the body in oil droplets [74].
Self-micro emulsifying systems have the ability to facilitate rapid oral absorption of the drug, which results in quick onset of action [75].
Absorption of drug from self-micro emulsifying systems formulation is not affected by food. The lipophilic contents of fatty diet, aids in absorption of drug from these systems [76].
Self-micro emulsifying systems can be easily manufactured at large scale as it requires simple and economical manufacturing facilities, such as simple mixer with an agitator and volumetric liquid filling equipment [77].
Surfactants of high hydrophilic lipophilic balance like polysorbate 80 are reported to increase the permeability of the drug when administered along with the formulation due to the loosening effect of these on tight junctions [78].
\n
In gastrointestinal tract fluid, diluted self-micro emulsifying systems undergo precipitation of drug. An essential for the lipid formulations is that they should allow keeping the drug in the solubilized form in the gastrointestinal tract. Advantage of lipid-based formulation is abolished due to the precipitation of the drug. The precipitation tendency of the drug on dilution is higher due to the dilution effect of the hydrophilic solvent. It thereby requires incorporation of polymers to minimize drug precipitation in-vivo [79, 80].
Liquid self-micro emulsifying systems are difficult during handling, storage and stability. Therefor formulating solid self-micro emulsifying systems seems to be a logical solution for these problems [81]. Another hurdle in the development of self-micro emulsifying systems and other lipid-based formulations is the lack of good established in-vitro models for the assessment of the formulations [79].
Conventional dissolution methods do not work, as these formulations potentially are dependent on digestion of lipid in the gut, earlier to release of the drug. In-vitro model replicating the digestive processes of the duodenum has been developed to mimic the condition [81]. This model also needs more clarification and validation before its strength are examined. Further, development can be based on in vitro–in-vivo correlations.
Lipid excipients containing unsaturated fatty acids and its derivatives are prone to lipid oxidation [81]. Inclusion of Lipid soluble antioxidant in formulation of capsule [82]. Polymorphism associated with thermo-softening lipid excipients requires specific process control in their application, in order to minimize polymorphic changes of the excipient matrix.
Self-micro emulsifying systems patent are shown in Table 4 [83, 84, 85, 86].
\nSr. no | \nSummary of invention | \nApplication | \nPatent number | \n
---|---|---|---|
1 | \nSelf-microemulsifying formulation containing taxoid, surfactant, and Co-surfactant [22]. | \nPoorly water soluble compounds Taxoids having high molecular weight, and slightly lipophilic. This patent enhances oral bioavailability of taxoids through self-emulsification. | \nEP1498143A1 | \n
2 | \nThe self-micro emulsifying Formulation consisting of poorly soluble or insoluble drug, vitamin E, a co-solvent, bile salt(s), TPGS, and a surfactant [72]. | \nIncreases bioavailability of poorly soluble drugs of paclitaxel and docetaxel. | \nEP1340497A1 | \n
3 | \nSelf-emulsifying pharmaceutical Composition containing a lipophilic drug, surfactant(s), and hydrophilic carrier(s) [73]. | \nImproves bioavailability of poorly soluble drugs such as cyclosporine, tacrolimus, ibuprofen, ketoprofen, nifedipine, amlodipine, and simvastatin. | \nEP2062571A1 | \n
4 | \nFormulation containing mitotane, propylene glycol monocaprylate, propylene glycol dicaprate, and polyoxyethylene sorbitanmonooleate [74]. | \nThe invention provides the SMES of mitotane, which overcomes the issue of its low solubility and low bioavailability. | \nEP2435022A2 | \n
Patented conventional SMES of lipophilic drugs.
Self-micro emulsifying systems drug delivery systems are effective approach for increase the bioavailability of poor water soluble drug. Currently, several formulations have been developed to produce modified emulsified formulations as alternatives to conventional self-emulsifying systems, which provide faster and enhanced drug release. Versatility of self-micro emulsifying systems could be proved if issues like method to predict solubilization state of the drug in-vivo, interaction of lipid systems with components of capsule shell and basic mechanism of transport of self-micro emulsifying systems through gastrointestinal tract are adequately addressed. Further research in developing self-micro emulsifying systems with surfactants of low toxicity and to develop in-vitro methods to better understand the in-vivo fate of these formulations can maximize the availability of self-micro emulsifying systems in market.
\nThe authors declare that there is no conflict of interests regarding the publication of this paper.
The electrical machines find wide and crucial applications in various industries and power plants. Condition monitoring of electrical machines is extremely significant for making the industrial processes more efficient with reduced downtime. Condition monitoring is heading as a real-time task, which requires maximum accuracy and embraces a gradual paradigm shift from legacy systems to modern Internet of Things (IoT) enabled systems at every level namely data acquisition, data processing, data integration and decision making. The uptime and efficiency of the plant operations shall be maximized through proper condition monitoring diagnostics and as well as preventive or predictive maintenance. The current condition monitoring systems make effective decisions using the knowledge repository, which is populated using various algorithms by the way of observing and storing the defective and unusual behaviour details of the machines. The major challenge faced by many industries is not only inadequate storage space but also the scalability when many machines inside the plant or operating at remote locations are to be monitored online and enormous amount of data have to be acquired from the machines for the interpretation of their behaviour at dynamic or abnormal operating conditions.
\nIn the late 90’s, very few online condition monitoring applications came into existence with the primary motive to collect vibration signals from various machines operating at different locations, but the analysis has been made considering each local operating environment to make effective decisions. This methodology leads to better preventive maintenance, but predictive maintenance is still a challenge. During those initial stages of online condition monitoring, the accessibility of such applications was through personal computers and laptops via World Wide Web. The scalability is the major issue in the current on-line condition monitoring applications. Over the period, due to the advancements in networking technologies, higher data rates for communication have paved a path to expansion in the field of cloud services. Especially, the industries which are facing the challenges such as inadequate storage space for data and scalability, the cloud environment will provide appropriate solution to those issues.
\nThe cloud based model must be designed to handle operations by various industries without any hurdle to exchange data due to heterogeneous nature. The cloud environment provides inherent dynamic scalability for the operations of electrical machines at different locations at different operating conditions. Cloud computing does not require global standard architectures, and it does not necessarily need a standard, open, general purpose protocol. Furthermore, cloud computing supports interfaces that are syntactically simple, semantically restricted and of high-level. The cloud environment provides an added value of being able to share and compare the local machine condition data with other similar machines across the plant, or with other machines at multiple plants wherever they are located.
\nThe growth of data analysis methods such as statistical, signal processing and machine learning techniques has moved condition monitoring of electrical machines towards the regime of predictive maintenance with the application of predictive analytics. Integrating the predictive maintenance techniques with the IoT enabled technologies will enable the industries to avoid unnecessary equipment replacement and improve process safety, availability and efficiency. Predictive maintenance adopted in industries employs predictive analytics to detect the problems well ahead to the occurrence of failures using which the corrective measures are planned. Prediction avoids unexpected process failures and prolongs the life of the system. In condition monitoring, though threshold estimation has more significance, it has not yet been given due consideration and thousands of false alarms are generated in dynamic operating conditions due to adoption of default threshold levels. The precise and faster short-term forecasting of machine’s physical signals predict the probability of failures and intensity of deterioration during abnormal conditions and provide performance optimization under normal or dynamic operating conditions. Condition monitoring stands effective only when the process of extracting information from the data becomes faster with more details. In this chapter, a new scalable and reliable model has been investigated to perform online condition monitoring of multiple machines in real-time industrial conditions and to perform predictive maintenance to enhance the process coordination and fault tolerance in industrial automation.
\nThe statistical classification based vibration analysis algorithm has been developed as a Web service and deployed as a cloud service to demonstrate real-time condition monitoring of electrical machines. The proposed cloud based condition monitoring system collects the vibration data of machines from various locations and processes the same in the cloud by comparing the data of one machine with the data of other similar machines for reliable and effective decision making. These features of interfaces are underlying factors for rapid adoption of cloud computing services in the condition monitoring applications.
\nMore research works regarding condition monitoring and predictive analysis are carried out for accurate assessment and prediction of machine conditions in real-time. The prediction models designed for monitoring real-time operation of electrical machines need to be robust and online in order to make accurate and faster data prediction.
\nIt is hard to fulfill the practical requirement of application specific scientific approaches for the industries while performing real time data analysis and condition assessment towards preventive maintenance as the machines are operating at different environmental conditions. Diego Galar et al. [1] have cited that single valued thresholds provided by the manufactures are not suitable for fault identification under non-stationary operations, environmental changes and aging. The vibration severity characterized by ISO 10816 has been specified as a static threshold suitable for new machines and said to provide incorrect reference for machines in use. Instead, the authors provide dynamic thresholds adaptive to operational conditions as a better solution for SMART maintenance.
\nContinuous monitoring and measuring of machine parameters such as vibration, temperature, etc., with and without external disturbances will lead to make proper decisions for effective maintenance and thereby prolong the useful life and reliable operation of electrical machines. Recently, smart Internet of Things technologies are evolving for effective condition monitoring of electrical machines. IoT enables online monitoring of the machine as it runs and data have been acquired by an embedded device or a gateway and transmitted to a server for analysis and maintenance scheduling. The practical challenges faced by maintenance engineers are the introduction of new technologies for the enhancement of plant productivity, methods of data acquisition and analysis, inconsistent outcomes and shortage of resources. R. Kirubashankar et al. [2] has explained about Internet based automation architecture for the control of the devices and equipments of process plants for optimal control and reduced unplanned downtime. A Web based architecture has been proposed for the control of devices remotely over Ethernet with Programmable Logic Controller (PLC) and Supervisory Control and Data Acquisition (SCADA) system networked in Virtual Private Network (VPN). Larry Combs [3] has stated that the functionality and reliability of conventional SCADA gets enhanced if it is hosted as Software as a Service in cloud platform. IoT enabled condition monitoring will identify potential problems using sensors and able to take necessry preventive measures before any issues occur and hence prevent damages and reduce maintenance costs. The changes in vibration, temperature etc., have been tracked by sensors and any issues such as misalignment, imbalance etc., shall be detected and accordingly service maintenance is schedueled automatically ahead of time to prevent failures thus avoiding unplanned downtime.
\nOmid Givehchi et al. [4] had designed a general cloud based architectural model that allows automation functions in industries to be offered as services from a dynamic infrastructure. The authors have envisaged the importance of cloud solution for the control and field levels of automation. The physical devices at these levels are integrated by encapsulating the services and functions inside the delivery standards of cloud. Cloud computing is seen as solution to provide platform for integration of growing information technologies such as Internet of Things, Service Oriented Architectures and mobile computing. Omid Givehchi and Jasperneite [5] have delivered the Virtual PLC as ‘Control as a Service’ through Microsoft Azure cloud environment. Omid Givehchi et al. [6] have illustrated about the development of Virtual PLCs on the Virtual Machines of the private cloud created using VMware’s vCloud suite.
\nCloud technology can be applied in two ways for automation of industrial processes. They are collaborative application development and real time publishing of data to the cloud server for remote monitoring and control. Though real time publishing is adopted for remote monitoring, collaborative applications also has equal significance. This is because of the option available to decide the factors of design, customization, updates and changes in the system before deployment. Also collaboration allows multiple consumers to monitor the process data simultaneously. This is highly required in places where the systems are inherently distributed such as irrigation systems, wind farms, cell towers, agriculture etc. The SCADA system maintained as a central monitoring system in local network, when taken to cloud server with collaborative software tools can aid in easy information exchange at multiple locations. The real time publishing can be made so easy in SCADA using SaaS (Software as a Service) in cloud. The software running the application will be on cloud server enabling easy and secured data publishing and data request. This also sends the data to multiple clients such as iPad, smart phones or other networks. The SCADA built in cloud with software tools providing collaboration leads to the creation of new business model using direct and shared access of multiple processes to a control expert. Further, on the data received from industrial systems, analysis can be made in the server side and feedback can be provided [7].
\nIn condition monitoring, the comparison of the data acquired with that of baseline standards is a widely adopted strategy [8]. NI in its artefact of fleet wide monitoring emphasizes the importance of continuous and automated data collection from industrial assets in order to realize meticulous comparative results so that real-time maintenance decisions are improved significantly. Such kind of maintenance strategy could successfully be achieved only with IoT based condition monitoring of industrial assets executed in cloud platform [9]. The cloud can be of public, private or hybrid nature. Fran Dougherty, CTO of the Worldwide Incubation Enterprise and Partner Group of Microsoft had appreciated the use of private and public clouds by industries for innovation, scalability and business growth in the special report composed by Jim Montague [10]. However, hybrid cloud was considered to be the best option by him, as industries can choose the type of analysis dynamically as per the requirements. Advantech in its white paper [11] has discussed on the importance of the implementation of cloud-based SCADA system using Industrial IoT and points out that the adoption of cloud offers pervasive analytics and decisions additionally irrespective of the hardware used and thus making Industry 4.0 effective. Steve Lacey [12] while discussing the ground realities of condition monitoring in industries asserted the need for skilled technicians for predictive maintenance. The author perceived cloud based condition monitoring to expand the monitoring horizons and assure direct connectivity with maintenance specialists whose availability has always been a challenge and also hinted on the execution of cloud based condition monitoring in Schaeffler due to various benefits. New analysis techniques have been implemented when unknown signal patterns are observed at the user end.
\nThe plant-wide condition monitoring of rotating electrical machines have extensively been discussed by Mallikarjun Kande et al. [13]. The existing machine condition monitoring and industrial automation techniques have been reviewed and the application of artificial intelligence for machinery diagnostics has been perceived as the future scope. While discussing about on-equipment and on premise integration methods, the need for on-cloud monitoring using IoT gateway has been substantiated to meet the requirements of advanced diagnostics and data platforms for enhanced computation. The condition monitoring system and Distributed Control System are integrated over the cloud for continuous monitoring of the equipment with high update rates from the sensors and for effective diagnosis. The efficient integration of various data acquisition and other devices in real time demands lightweight and uniform communication standards.
\nA comprehensive investigation has been made [14] that shifts the focus from the monitoring of specific machine components for fault prognosis to an approach scanning the overall system execution in an integrated manner to deliver desired performance for the application in an optimum manner. The authors address the challenge in the determination of absolute vibration thresholds adaptive to the machine operating conditions for reliable condition monitoring. A statistical classification based signal decomposition algorithm has been proposed for segmented vibration signal analysis as a measure of improving the precision in condition monitoring of electrical machines.
\nIn this chapter, IoT based cloud services for real-time condition monitoring of electrical machines are proposed. It is focused towards estimation of vibration thresholds adaptive to the machine condition, which persuades to realization of incipient and critical abnormal conditions fully. Considering the immense raise towards the importance of predictive maintenance applications and connected IP based data acquisition devices, a generalized cloud framework is proposed to provide services for effective condition monitoring diagnostics and to maintain a knowledge repository for effective decision making with respect to maintenance scheduling. The main objective of this chapter is to explore real-time implementation of IoT enabled cloud services to formulate pre-emptive, strategic and operational decisions. The proposed IoT based model for vibration analytics of electrical machines addresses the challenges of data storage and scalability. A Web Application Framework has been developed by introducing cloud services for real-time data acquisition, data classification, data processing and decision making for effective condition monitoring.
\nThe statistical classification based signal decomposition algorithm discussed in [14, 15] identifies the denser vibrating levels of machine under dynamic operating conditions and enumerates cluster of thresholds adaptive to the operating conditions for quick and accurate prediction of abnormalities. This algorithm is integrated with IoT based model through LabVIEW client application to enable real-time condition monitoring of machines located anywhere whose data are acquired by sensors and transmitted to cloud storage. The cloud services which have been developed for data acquisition and processing are tested locally before deploying in the Django Web Framework and implemented in Google Cloud Platform. The results of the classification algorithm, i.e., the adaptive threshold class clusters pertaining to each machine are used to create contextual vibration references for making efficient and quick decisions in the condition assessment of machines of same type exposed to similar operating conditions.
\nAn effective cloud-based model is proposed to estimate the cluster of thresholds adaptive to various operating conditions of the electrical machines, to eliminate the identification of false failures or alarms, and to make decisions for effective maintenance scheduling. To make its implementation more general and scalable for real-time analysis, cloud services are introduced in different layers in accordance with Model-View-Template (MVT) pattern of Django Web Framework (
Cloud services are deployed for real-time data acquisition, data classification, data processing and decision making, which are highly interconnected services for effective condition monitoring of electrical machines. The proposed Software as a Service, Storage as a Service and Platform as a Service layers address the challenges of data storage and scalability while making analysis on the cluster of machines in an Industrial Environment. An experimental setup consisting of two DC motors each coupled to AC Generator operating at different locations is considered to evolve the proposed model for effective integrated monitoring and decision making. The threshold values estimated using cloud services are compared with that of the vibration analysis carried out on the machine beds locally using myRIO for data acquisition in LabVIEW ensures the integrity of the cloud-based model with assured scalability. Though security and big data processing overheads are encountered in the connected enterprises, employing cloud computing has been widely embraced by industries for its collaborative nature, optimized performance, better diagnostics, higher productivity and sustainability. IoT and cloud-based processing have been adopted for condition monitoring of multiple machines operating at different locations as they evolve as a better choice due to the attributes of cloud storage, flexible application development, data aggregation, scalability and platform of multiple services. The deployed cloud services eliminate the false identification of failures, which not only increase the availability of machines for intended operations but also reduce the maintenance cost.
\nThe proposed cloud services for machine vibration monitoring using IoT based framework have been modelled as a layered architecture as shown in Figure 1 and implemented on the experimental set up as shown in Figure 2. The main layers [16] of the proposed model are the Platform as a Service (PaaS) layer, which is Google Cloud Platform, Software or Application as a Service (SaaS) layer where all the proposed services are deployed and Storage as a Service layer where all the data stores have been maintained. In online condition monitoring applications, the sensors are used to acquire vibration signals and communicated to the cloud storage for further processing. The proposed framework will enhance the machine condition monitoring functionality with methodologies of scalable and platform independent data aggregation and collaborative analysis that the real-time industrial applications demand extensively.
\nCloud Services - Layered Architecture for Condition Monitoring Model.
Experimental Setup.
The cloud infrastructure provides the fundamental resources needed to share upper level platforms and services. The “Storage as a Service” layer (Model perspective) and the physical resources needed for the “Computational Engine” and for communication among services along with core services for condition monitoring (View perspective) form the basis for delivering Infrastructure as a Service (IaaS). Google App Engine is used as Platform as a Service (PaaS) in the proposed model that provides a conducive environment to implement the cloud services. Google Cloud provides a set of APIs to aid the interaction between cloud components and end user applications, to enhance scalability, and ease deployment and management.
\nThe Django Web Framework provides an environment to deploy the services developed in accordance with Model-View-Template pattern and the “google-cloud” API for Python provides interfaces for interacting with Google App Engine, which is configured as Python based cloud runtime environment. Software as a Service layer (SaaS) is for end users i.e., for the application developers. The services in this layer are typically accessed through Web portals using Templates. The condition monitoring services provided with this model are normally referred to as SaaS and implemented in Views of Django Web Framework. The content providers of various services using a separate thread of control for each service, which have been developed by the registered end users, i.e., participating industries have stored the data in different formats under heterogeneous environment. The server application in the cloud environment has the control to manage the databases maintained in the Storage as Service layer. With the App Engine, each participating industry can write their application code, test it on their local machines and then deploy on cloud environment.
\nGoogle App Engine is used for registering, uploading and accessing the condition monitoring services in the proposed cloud-based model for estimation of adaptive thresholds among various machines. The App Engine Software Development Kit (SDK) for Python is used to create and to link the services to the cloud. The location independent “google-cloud” client API as well as the shell SDK are providing the Python based cloud interfaces which can be accessed by the Django Web Services framework and Internet browser. The cloud applications need a configuration file i.e., “app.yaml” to deploy and run the application. The condition monitoring and decision making services configured using App Engine are easy to build, easy to maintain, and easy to scale as traffic and data storage needs grow. The services have been uploaded for ready to serve to any of number of machines located at distributed industrial environments.
\nThe Django “admin” is used to create a project named “ConditionMonitoring” using the command “django-admin startproject” with an application registered as “MonitoringApp”. The python script, “settings.py” defined within the project enumerates the default backend database and all the registered applications as detailed below:
\n#settings.py DATABASES = {\'default\': { \'ENGINE\': \'django.db.backends.mysql\', \'NAME\': \'VIBRATION_DB\', \'USER\': \'username\', \'PASSWORD\': \'password\', \'HOST\': ‘192.168.1.112’, \'PORT\': \'5000\',} INSTALLED_APPS = [\'django.contrib.admin\', ….., \'MonitoringApp\', ] | \n
The python script, “manage.py” defined within the project starts the Webserver, migrates and synchronizes and flushes the databases if required. The script, “urls.py” defines the URL patterns to link the Views. The Views render the request/response to the Templates as XML for processing and HTML for presentation. The application, “MonitoringApp” describes and defines the required services in the Views. In the proposed cloud framework, the Model represents the Storage as a Service layer (virtual storage), the View represents the Application as a Service layer (SaaS) and the Template represents the presentation tier which includes Computational Engine.
\nThe logical schema for various database tables used in real-time condition monitoring to store the machine data, repository for previous decisions, maintenance schedules, data pertaining to historical conditions (i.e., decision support system) are implemented as Model entities under Django Web Framework, where each Model maps to a single database table dynamically. Model is the single definitive source of information about the data and it is defined in the “models.py” script. The metadata of the vibration signals acquired for condition monitoring of every machine, the metadata for the specification of machines of all participating industries and the metadata used for “Decision Support System” are described in the Model as follows:
\nVibration Signals Metadata
\n[Component Name, Operating Condition, Disturbance Nature, Input Current, Vibration, Samples]
\nMachines Metadata
\n[Machine_ID, Machine_Type, Rated Voltage, Rated Current, Rated Speed]
\nDecision Support System Database (Repository)
\n[Machine_ID, No. of Classes, Range of Classification, Class Width, Total Oscillations, Upper Threshold Class Cluster, Lower Threshold Class Cluster, Excess Positive Slopes, Excess Negative Slopes]
\nIn general, the View retrieves data according to the path parameters defined in the “URL patterns” list, loads a template, renders the template with the retrieved data and returns the HTTP Response instance as output. Each View is a python service and an appropriate View is chosen by examining the URL that is requested as per the configurations made in the URL patterns. The codes pertaining to acquisition of vibration data, statistical classification algorithm, threshold estimation etc., are defined as python functions in the View’s sub-directory of the application.
\nThe vibration data of the DC motor in the specially created experimental setup have been acquired when started at no load condition as well as loaded by AC Generator at fixed load changes. It is well known that the factory floor generally has lot of machines running together. Thus to create a similar field condition of the factory floor, an additional motor was installed nearer to the DC motor in the experimental setup and the shaft vibration data are again acquired for the stated conditions. The acquired vibration data under the operating conditions of starting to no load speed with and without external disturbance and loading are streamed to cloud through IoT2040 gateway. The LabVIEW client application enables collaborated real-time condition monitoring of any machine by integrating the non-stationary vibration analysis algorithm with a cloud service. The analysis results updated to the decision-making service lead to effective condition monitoring and make the maintenance of other connected devices/machines automatic and perfect scheduling. The updated results create contextual vibration references for assessing the condition of any other machine of same type that has been exposed to similar operating conditions.
\nA project can be configured with one or several template engines (
<html> <body bgcolor="#bg99FF"> <p>Type:{{Machine_Type}}</p> <p>AcquisitionDevice:{{ SensorList }}</p> <p>Machine_ID: {{Machine_ID}}</p> <p>OperatingCondition:{{ Disturbance / Standalone / loading }}</p> <p> DisturbanceNature: {{Constant Speed}}</p> <p> Vibration: {{ samples .tdms }}</p> <p>Speed:{{RatedSpeed}}>/p> </body> </html> | \n
The corresponding URL patterns entry is mentioned in the “urls.py” as follows:
\nfrom django.contrib import admin from django.urls import path from import .views urlpatterns = [ path(\'admin/\', admin.site.urls), path(\'dataAquisition/\',views.acquireVibrationData), path(\'dataframe1/\',views.ConditionMonitoring_Standalone), path(‘dataframe2/’,views.ConditionMonitoring_Disturbance), patch(‘dataframe3/’,views.ConditionMonitoring_Loading), path(\'analysis/\',views.StatisticalAnalysis), …..] | \n
To communicate between various services, the data acquisition as well as estimated threshold values have been generated as XML for which the corresponding schemas are defined in the View’s subdirectory. The XMLized representation of the dataframe generated by the ConditionMonitoring_Standalone service is given below:
\n<!--Dataframe representation--> <?xml version="1.0"?> <Dataframe> <Machine_Type>DC Motor</Machine_Type> <Machine_ID>EE-M5864</Machine_ID> <OperatingCondition>Standalone – Starting to No load speed </OperatingCondition> <DisturbanceNature>NIL</DisturbanceNature> <Vibration>snl.tdms</Vibration> <Speed>1500</Speed> </Dataframe> | \n
In accordance with the response of the “ThresholdEstimation” service, the “DecisionMaking” service generates appropriate maintenance schedules in the XML form and helps in segregating false identification of failure status. If the attributes of any data store have been changed, the corresponding Model updates the entries in the XML representation dynamically through its Views.
\nUpon testing the Condition Monitoring Django Application in the local machine with the local SQL server running in the backend, the application is deployed in the Google App Engine Standard Environment (
Google Cloud provides Cloud SQL as Storage as a Service (SaaS), which supports all the database transactions with respect to the application to be deployed in the Cloud, which have been controlled by user defined threads of control.
\nThe configurations for the Database dictionary in “settings.py” with MySQL are set accordingly as per the Cloud SQL Instance connection name, database user, password and port.
\nBefore deploying the application, verification of the same is carried out in the local development environment by following the standard Web application execution procedure defined by Django Web Framework. The migrations are also carried out to set up the Models.
\nAll the static files of the application are gathered into a single directory by executing the command “python manage.py collectstatic”. These static files are moved to the production site while deploying the application to the Google Cloud. The “requirements.txt” file is created to mention the dependencies and “app.yaml” which contains the environment, runtime and entry point is also created and finally, the application can be deployed on to the Google Cloud by executing the command “gcloud app deploy”. Threads of control are introduced to populate the content provider by initiating the services in parallel mode to gather vibration data, machine data, to analyse the vibration signal data and to store the historical information in the “Decision Support System” data store for validation and further processing.
\nA new vibration analysis algorithm developed with statistical classification and clustering extracts the detailed signal features and estimates adaptive thresholds [14]. This signal decomposition algorithm, which forms the base for computational engine of the proposed IoT based cloud services model tracks the vibration signal transitions between the classes at multiple levels of amplitudes and computes the number of oscillations between the levels. While classifying the signal, the amplitude is segmented into a desired set of classes of equal and desired width. A transition matrix with ‘n’ number of classes and signal transitions is formulated out of the statistical classification of the data read from the cloud and streamed to DIAdem [18] as “.tdms” file by client application developed in LabVIEW platform for computation of the oscillation nature of the machine vibration.
\nIn the proposed vibration analysis technique, the number of signal transitions has been considered to calculate the oscillations between every class and other classes to extract the threshold levels of vibration. The transition of the signal from a lower class to higher class and vice-versa are accounted as positive and negative slope respectively in the transition matrix. The analysis has been carried out by considering the transition matrix and progressing through the upper diagonal matrix row-wise and lower diagonal matrix column-wise elements or its vice-versa. The ‘n’ column vectors of upper diagonal matrix give the signal transitions from a class corresponding to the row to class of the diagonal element and ‘n’ row vectors of the lower diagonal matrix represent the signal transitions from class of the diagonal element to lower class corresponding to the column. The proposed algorithm determines the oscillations in the real time non-stationary vibration signal at multiple class levels using statistical classification of the signal amplitude as well as the transition matrix and delivers the features as Oscillation Matrix. The dominant classes of the Oscillation Matrix having higher count of oscillations with that of lower classes have been clustered together and identified as upper and lower threshold class clusters.
\nThis algorithm is validated on the vibration signals acquired by IoT2040 gateway which is integrated with Google Cloud Platform and offered as cloud service in real time while testing cloud based condition monitoring. The computational engine has been integrated with the signal analysis to add more investigations towards the changes happening in the vibration during different operating conditions and offer adaptive thresholds for condition monitoring.
\nTo realize the effectiveness of the vibration thresholds estimated from the IoT based data analysis, a comparative analysis has been made with the results of vibration data acquired through myRIO. The same experimentation has been carried out for acquiring the shaft vibration data using myRIO1900 [19] as acquisition device and tri-axial accelerometer (ADXL345) as vibration sensor. The data acquired in both cases have been fed to the computational engine for carrying out statistical classification analysis for fixing the clusters of vibration thresholds precisely at the following dynamic operating conditions:
Starting to no load speed and load changes in standalone mode of operation
Starting to no load speed with induced external disturbance
In all test cases and operating conditions, the single valued vibration threshold is replaced with a cluster of upper and lower thresholds for safe operation of a machine. This has been implemented on 3,96,000 samples of vibration signal acquired from the DC motor under the stated conditions. The acquired data are logged in an excel file and imported to the computational engine in which the statistical classification technique is implemented using NI DIAdem [18]. This technique has been tested on the DC motor shaft vibration signals at the above stated operating conditions using different classification criteria. In each case, the same vibration signal is segmented to study about the oscillations pattern.
\nThe shaft vibration signal of DC motor pertaining to the operating conditions of starting to no load speed (standalone and disturbance conditions) and loading (standalone condition) have been acquired by myRIO application developed using LabVIEW FPGA and RT programming through the tri-axial accelerometer ADXL345 having sensitivity of 256 LSB/g. The vibration data during the same operating conditions are acquired by IoT2040 gateway [20] from a Piezo electric sensor through serial interface under the measurement unit of ‘g’ and transferred the scaled value in LSB to cloud simultaneously. The upper and lower threshold class clusters are identified with the application of signal processing algorithm on the vibration signal and the non-stationary vibration data corresponding to different operating conditions are characterized using the transition matrix obtained from the statistical classification.
\nThe acquired data pertaining to this operating condition with respect to myRIO and IoT Gateway (as shown in Figure 3) applications have been fed to the computational engine for statistical classification and analyzed with different classification configurations. The observed signal peak values and the configuration settings assumed for classification in each case are listed in Table 1.
\nShaft Acceleration Acquired by IoT Gateway – Standalone Condition.
Observed Peak Values | \nClassification Configuration | \n|||
---|---|---|---|---|
Maximum Amplitude | \nMinimum Amplitude | \nRange | \nNo. of Classes | \nClass Width | \n
myRIO | \n||||
368 LSB | \n-286 LSB | \n654 | \n12 | \n54.5 | \n
IoT Gateway | \n||||
377 LSB | \n-281 LSB | \n658 | \n12 | \n54.83 | \n
Measured Values and Classification Configuration - Standalone Starting to No Load Speed
The vibration analysis is carried out by the computational engine and the estimated upper and lower threshold clusters are rendered to the “VibrationAnalysis” template, which can be accessed through the cloud environment by specifying the URL: “\n
Adaptive Threshold Class Clusters – Standalone Condition
Similar to the standalone mode, the experiment has been carried out with an external disturbance using IoT and myRIO based data acquisition systems and the shaft vibration pattern has been examined. While performing analysis on the vibration signals with mechanical disturbance, the observed peak values of the vibration signal and the configuration settings made for analysis are listed in Table 2.
\nObserved Peak Values | \nClassification Configuration | \n|||
---|---|---|---|---|
Maximum Amplitude | \nMinimum Amplitude | \nDifference | \nNo. of Classes | \nClass Width | \n
myRIO | \n||||
453.6 LSB | \n-296.3 LSB | \n749.90 | \n14 | \n54.01 | \n
IoT Gateway | \n||||
460.19 LSB | \n-296.08 LSB | \n756.27 | \n14 | \n54.01 | \n
Measured Values and Classification Configuration - Starting to No Load Speed under External Disturbance.
Using the resulted transition matrix, the oscillations existing between every class and its lower classes are calculated and the dominant classes with more percentage of oscillations measured during the presence of external disturbance have been identified to form the upper threshold class cluster. To form the lower threshold class cluster, every class of the upper threshold class cluster that has made 65 percent or more number of oscillations cumulatively with its lower classes are considered and the results are rendered to the “Disturbance Condition” template, which can be accessed through the cloud environment by specifying the URL:
Adaptive Threshold Class Clusters – Disturbance Condition.
The shaft acceleration i.e., the vibration signals acquired by the myRIO and IoT based cloud applications during the load changes made at standalone running condition of the DC machine are shown in Figures 6 and 7. The observed characteristics and analysis settings used in statistical classical algorithm for condition monitoring analysis in both cases are presented in the following Table 3.
\nShaft Acceleration acquired by myRIO during Loaded Condition.
IoT based acquisition of Shaft Acceleration during Loaded Condition.
Observed Peak Values | \nClassification Configuration | \n|||
---|---|---|---|---|
Maximum Amplitude | \nMinimum Amplitude | \nDifference | \nNo. of Classes | \nClass Width | \n
myRIO | \n||||
389 LSB | \n-292 LSB | \n681.00 | \n13 | \n52.3 | \n
IoT based Cloud Application | \n||||
393.51 LSB | \n-288.49 LSB | \n682.00 | \n13 | \n52.4 | \n
Measured Values and Classification Configuration – Load Changes.
The results of upper and lower threshold class clusters obtained from the implementation of the signal decomposition algorithm are rendered to the “LoadingCondition” template, which can be accessed through the cloud environment by specifying the URL: “\n
Adaptive Threshold Class Clusters – Loaded Condition.
The analysis carried out using the statistical classification based signal decomposition technique for different machine operating conditions is based on the machine vibrations occurring within the permissible limits. The consistency in the total number of oscillations and marginal deviations in the threshold class clusters reveal the flexibility of choosing any of the desired classification criteria during real time implementation. Contrary to the currently adhered thresholds, the adaptive cluster based thresholds possess the significance of tracking the condition of the machine without demarcating the naturally occurring vibration variations as crossing limits during condition monitoring. Hence, this technique avoids false identification of failures caused due to incorrect thresholds and also identifies the ignored failures. This cloud based analysis of random vibration signal is a perception that has been believed to offer better alternative for deriving decisions for efficient condition monitoring when other analysis techniques find challenges in bringing out precise and faster solutions for condition monitoring under dynamic conditions.
\nThe DC motor shaft vibration pattern has been examined by acquiring the vibration signal through IoT2040 gateway using Python interface and myRIO using LabVIEW interface considering the same machine operating conditions. The vibration signals acquired by various data acquisition devices have been analyzed by the statistical classification based signal decomposition algorithm considering three different modes of application platform as given below:
An independent vibration analysis application in LabVIEW platform (myRIO based analysis)
LabVIEW application integrated with IoT service (IoT based analysis)
LabVIEW application hosted as a cloud service in Google Cloud Platform (Cloud based analysis)
The upper and lower threshold class clusters of DC motor’s shaft vibration determined using the above applications are furnished in Table 4 which define the scope of the amplitude levels between which majority of the shaft vibrations oscillate during the specified operating conditions. In either case of analysis, the proposed algorithm has uniformly brought out the changes that had happened in the vibration pattern and upholds the fact of fixing thresholds adaptive to the operating condition. The values of deviation between the deduced threshold levels considering the same and various operating conditions are enlisted in Tables 5 and 6. The margin of deviations between the threshold class clusters specific to an operating condition determined using IoT [14] and cloud based analysis methods with reference to the amplitude range of vibration signal measured using myRIO (Table 5) are observed to be negligible as compared to the deviation between threshold class clusters identified by any particular analysis method for different operating conditions (Table 6). In each of the cases considered, the incipient faults or abnormalities during any of the operating conditions can be diagnosed precisely by analysing the margin of deviations in the threshold class clusters. The difference in the deviation values observed from Tables 5 and 6 reveals that the threshold class clusters obtained by the analysis of data acquired either from IoT device, cloud service or from myRIO device do not lead to incorrect decisions and tends to recognize the change of operation conditions without ambiguity.
\nStarting to No Load Speed (Standalone Condition) | \nStarting to No Load Speed (Disturbance Condition) | \nLoading (Standalone Condition) | \n||||||
---|---|---|---|---|---|---|---|---|
Cloud Based Analysis | \nIoT Based Analysis | \nmyRIO Based Analysis | \nCloud Based Analysis | \nIoT Based Analysis | \nmyRIO Based Analysis | \nCloud Based Analysis | \nIoT Based Analysis | \nmyRIO Based Analysis | \n
Upper Threshold Class Cluster | \n||||||||
{ 20.58 75.41 130.25 185.08 239.91 } | \n{ 15.20 69.79 124.37 178.95 233.54 } | \n{ 13.88 68.46 123.05 177.64 232.23 } | \n{ 1.03 55.05 109.06 163.08 217.10 } | \n{ 0.03 53.67 107.32 160.96 214.60 } | \n{ -1.68 51.88 105.46 159.03 212.61 } | \n{ 0.08 52.54 105.01 157.48 209.94 } | \n{ -0.76 52 104.76 157.53 210.30 } | \n{ -3.79 48.67 101.13 153.60 206.07 } | \n
Lower Threshold Class Cluster | \n||||||||
{ -143.91 -89.08 -34.25 } | \n{ -148.54 -93.95 -39.37 } | \n{ -149.88 -95.29 -40.70 } | \n{ -161.03 -107.01 -52.99 } | \n{ -160.89 -107.25 -53.60 } | \n{ -162.41 -108.84 -55.26 } | \n{ -157.32 -104.86 -52.39 } | \n{ -159.07 -106.30 -53.53 } | \n{ -161.18 -108.72 -56.25 } | \n
Adaptive Threshold Clusters Identified by Different Analysis Methods.
Starting to No Load Speed (Standalone Condition) | \nStarting to No Load Speed (Disturbance Condition) | \nLoading (Standalone Condition) | \n|||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Cloud Based Analysis | \nIoT Based Analysis | \nCloud Based Analysis | \nIoT Based Analysis | \nCloud Based Analysis | \nIoT Based Analysis | \n||||||
D | \n%D | \nD | \n% D | \nD | \n% D | \nD | \n% D | \nD | \n% D | \nD | \n% D | \n
6.7 | \n1.0 | \n1.32 | \n0.2 | \n2.71 | \n0.4 | \n1.71 | \n0.2 | \n3.87 | \n3.03 | \n0.6 | \n0.4 | \n
7.0 | \n1.1 | \n1.33 | \n0.2 | \n3.17 | \n0.4 | \n1.79 | \n0.2 | \n3.87 | \n3.33 | \n0.6 | \n0.5 | \n
7.2 | \n1.1 | \n1.32 | \n0.2 | \n3.6 | \n0.5 | \n1.86 | \n0.2 | \n3.88 | \n3.63 | \n0.6 | \n0.5 | \n
7.4 | \n1.1 | \n1.31 | \n0.2 | \n4.05 | \n0.5 | \n1.93 | \n0.3 | \n3.88 | \n3.93 | \n0.6 | \n0.6 | \n
7.7 | \n1.2 | \n1.31 | \n0.2 | \n4.49 | \n0.6 | \n1.99 | \n0.3 | \n3.87 | \n4.23 | \n0.6 | \n0.6 | \n
Deviations of Adaptive Threshold Clusters of Same Operating Condition from myRIO Based Analysis.
Disturbance Condition (Deviation D) | \nLoading at Standalone Condition (Deviation D) | \n||||
---|---|---|---|---|---|
Cloud Based Analysis | \nIoT Based Analysis | \nmyRIO Based Analysis | \nCloud Based Analysis | \nIoT Based Analysis | \nmyRIO Based Analysis | \n
20 | \n15 | \n16 | \n21 | \n16 | \n18 | \n
20 | \n16 | \n17 | \n23 | \n18 | \n20 | \n
21 | \n17 | \n18 | \n5 | \n20 | \n22 | \n
22 | \n18 | \n19 | \n28 | \n21 | \n24 | \n
23 | \n19 | \n20 | \n30 | \n23 | \n26 | \n
Deviations of Threshold Class Clusters Between Different Operating Conditions for specific method of analysis
In spite of some minor deviations in the threshold values determined for every operating condition during different methods of analysis, the substantial differences in the threshold class clusters identified due to the change of operating conditions in all the analysis methods validate also the fact that the incipient faults or abnormalities (during any of the operating condition or change in the operating conditions) can be diagnosed without ambiguity using the proposed statistical classification algorithm integrated with the cloud environment.
\nIn this work, the actual machine condition is monitored in online mode by continuous acquisition of vibration signals and simultaneous estimation of the threshold levels at different operating conditions using developed cloud services of data acquisition and data processing. The machine’s vibration signature will not change over a period of time until some disturbance or loading has occurred. During abnormal conditions, the vibration amplitude may increase or decrease from the normal value. Integrating the actual deviation from the mean values of the upper and lower threshold clusters with historical data for the same or similar machine under the same operating condition facilitates to evaluate the current condition of the machine and hence to schedule suitable maintenance action.
\nIt is observed from the analysis that the vibration threshold class clusters identified for any particular operating condition cannot be maintained as an alarm for monitoring of machines at all conditions to decide appropriate maintenance schedules. Further the analysis reveals the fact, a neighboring machine running at constant speed of 1500 rpm creates a vibration effect equivalent to that of loading in other machines which substantiates the fixation of the operational limits for mechanical and electrical loading, speed and torque capacity etc., during machine maintenance.
\nThe consolidated results obtained from the implementation of the proposed algorithm under unequal and equal classification criteria in local and cloud platforms illustrate the reliability of the analysis technique in yielding consistent results. As illustrated above, the characteristic of the technique to extract the signal features in such a way that they identify the unremarkable disturbances in the operating conditions, and effects of change of operating conditions enable fixation of cluster based thresholds adaptive to machine operating conditions rather than fixed and ambiguous threshold levels. Moreover, the consistency of the observed threshold values under any of the classification parameters ensures the efficiency of the technique. The technique employed on shaft vibration signal at different operating modes has identified ignored disturbances, intensity and characteristics of such disturbances and incipient changes in the operational behavior both in the on premise analysis and cloud based analysis. Thus the proposed IoT based cloud deployment will help to prescribe the operational constraints for machines in real time applications so that the machine can deliver improved performance and have extended lifetime.
\nIn either case of analysis based on myRIO, IoT device or Cloud based, the investigation uniformly brings out the changes that had happened in the vibration pattern and upholds the fact of fixing thresholds adaptive to the operating condition. The integration of vibration sensors and actuators through Python and LabVIEW interfaces with cloud in real time ascertains generic, interoperable and ubiquitous computational nature of the model for implementation of effective condition monitoring. In this research, the convergence of cloud and IoT technologies for analysis of real-time systems has been brought into implementation for condition monitoring of electrical machines to support scalable and interoperable data exchange with features of flexible and collaborative analytics, fixation of adaptive alarms with contextual thresholds and control of multiple machines in real-time operating environment. The model gives collaborative access to machine data from any geographical location for analysis and decision making.
\nIn summary, the cloud-based vibration monitoring model implemented in Google Cloud Platform offers services for
Data acquisition from the sensors mounted on the shafts of the DC motors.
Data storage to store the enormous amount of acquired signal data from multiple sensors.
Data classification of vibration signals for effective statistical analysis to estimate adaptive cluster of thresholds and
Decision making for condition assessment.
These services have been offered on demand over the Internet to utilize the reliable service of the machines in a persistent way. The computational engine, which is included in the model performs inherent statistical analysis of the vibration signals to estimate the cluster of thresholds adaptive to various operating conditions. The services have been deployed without any limitation in a cloud environment and the industrial applications can share information using the deployed services from anywhere on demand basis. The threshold values estimated using cloud services are compared with that of the vibration analysis carried out on the machine beds locally using myRIO for data acquisition in LabVIEW ensures the integrity of the cloud-based model with assured scalability.
\n\n Internet of Things Reconfigurable Input Output International Organization for Standardization Supervisory Control and Data Acquisition Programmable Logic Controller Virtual Private Network Laboratory Virtual Instrument Engineering Workbench Google Cloud Platform Model-View-Template Platform as a Service Software as a Service Infrastructure as a Service Application Programming Interface Technical Data Management Streaming format Software Development Kit Hypertext Transfer Protocol Hypertext Markup Language Uniform Resource Locator eXtensible Markup Language Structured Query Language Software Development Kit Field-Programmable Gate Array Real Time Acceleration due to gravity (9.81 m/s2) Least Significant Bit per g Percentage Deviation
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',metaTitle:"Horizon 2020 Compliance",metaDescription:"General requirements for Open Access to Horizon 2020 research project outputs are found within Guidelines on Open Access to Scientific Publication and Research Data in Horizon 2020. The guidelines, in their simplest form, state that if you are a Horizon 2020 recipient, you must ensure open access to your scientific publications by enabling them to be downloaded, printed and read online. Additionally, said publications must be peer reviewed. ",metaKeywords:null,canonicalURL:null,contentRaw:'[{"type":"htmlEditorComponent","content":"Publishing with IntechOpen means that your scientific publications already meet these basic requirements. It also means that through our utilization of open licensing, our publications are also able to be copied, shared, searched, linked, crawled, and mined for text and data, optimizing our authors' compliance as suggested by the European Commission.
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. 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