Fragment of hierarchical classification of faults for FADEC
\\n\\n
Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\\n\\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\\n\\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\\n\\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\\n\\nThank you all for being part of the journey. 5,000 times thank you!
\\n\\nNow with 5,000 titles available Open Access, which one will you read next?
\\n\\nRead, share and download for free: https://www.intechopen.com/books
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Preparation of Space Experiments edited by international leading expert Dr. Vladimir Pletser, Director of Space Training Operations at Blue Abyss is the 5,000th Open Access book published by IntechOpen and our milestone publication!
\n\n"This book presents some of the current trends in space microgravity research. The eleven chapters introduce various facets of space research in physical sciences, human physiology and technology developed using the microgravity environment not only to improve our fundamental understanding in these domains but also to adapt this new knowledge for application on earth." says the editor. Listen what else Dr. Pletser has to say...
\n\n\n\nDr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\n\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\n\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\n\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\n\nThank you all for being part of the journey. 5,000 times thank you!
\n\nNow with 5,000 titles available Open Access, which one will you read next?
\n\nRead, share and download for free: https://www.intechopen.com/books
\n\n\n\n
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"1507",leadTitle:null,fullTitle:"Toxicity and Drug Testing",title:"Toxicity and Drug Testing",subtitle:null,reviewType:"peer-reviewed",abstract:"Modern drug design and testing involves experimental in vivo and in vitro measurement of the drug candidate's ADMET (adsorption, distribution, metabolism, elimination and toxicity) properties in the early stages of drug discovery. 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Reliability, safety and durability represent important properties of modern aircraft, which is necessary for its effective in-service use.
The reason of the main hazard for aircraft are both random and determined negative influences rendering the controlled object during its use. Faults, failures, disturbances, noises, influences of environment and control errors represent the objectively existing stream of random negative influences on the object.
Statistically, in the recent years the majority of aircraft incidents are connected with the human factor and late fault detection in plane systems. In this regard, requirements to flight safety which demand development of new methods and algorithms of control-and-condition monitoring/ diagnostic for complex objects raise every year. The analysis of modern gas turbine engines has shown that most faults appears in the engine itself and its FADEC (40-75% for FADEC, Figure 1).
The percentage of faults for FADEC depends on the achieved values for no-failure operation indicators of the engine and FADEC.
During the development of FADEC, it is necessary to adhere to the principles and methods guaranteeing safety and reliability of aircraft in use to guarantee proper responses in all range of negative influences.
Full information on its work is necessary for complete control of a condition of the engine:
Reliable detection of a fault cause providing decision-making on a technical condition of gas turbines;
Reliable diagnosis and localization of faults and negative influences are necessary for definition of technical condition of gas turbines for the purpose of providing a reconfiguration and functioning of its subsystems [1, 2].
Faults percentage for engines and FADEC
The hardware for condition monitoring of measurement channels in many cases allows to detect only catastrophic (breakage or short circuit) faults, i.e. their stochastic properties on time of the process observed in one object and on a set of objects are not distinguishable [3]. The criteria of warning messages on faults appearance are based mainly on determined logic operations and distinguish between only two conditions: "operational" (fully operational) or "fault". In this chapter, hierarchical fuzzy Markov models for quantitative estimation of system safety of gas turbines taking into account the monitoring of cause-effect relations are considered. Transition from two-valued to fuzzy logic for estimation of degradation indexes and the analysis of fault developments for the gas turbine and its FADEC is considered for this purpose.
Complex diagnostics of the power plant is proposed to be carried out on elements and units, using the hierarchy analysis method [4, 5]. First, decomposition into independent subsystems of various hierarchy levels is carried out on structural features. Similarly, the power plant and its systems are represented in the form of hierarchy of elements and blocks.
This approach enables cause-effect relationships to be identified on the hierarchy structure of a system.
In Figure 2, the hierarchical structure of states of the power-plant is shown. The power plant is represented in the form of a hierarchical structure as the complex system consisting of subsystems and elements (units) with built-in test/monitoring functions, according to the distributed architecture. For this purpose, the power plant decomposition might be performed into independent subsystems with various levels of hierarchy on structural and functional features in the following way:
Control and monitoring system (FADEC);
Hydro mechanical system (actuators);
Fuel system;
Start-up system;
Lubricant oil system;
Drainage system, etc.
The hierarchy analysis allows to utilize the state model on the basis of faults development which enables the system state to be estimated at each level of the hierarchy.
The mathematical model of states is represented as
where S is state vector,
G is hierarchy of system faults,
F is quantitative estimate of faults,
L is set of fault influence indexes,
R is mutual influence system of faults.
The depth of hierarchy G is referred to as h, and h = 0 for the root element of G.
For G the following conditions are satisfied:
There is splitting of G into subsets of hk, k = 1 … n.
From
From
For every
The sets of hi are the hierarchy levels, and function ωx is a function of fault priority of one level concerning the state of the power-plant x. Notice that if
The hierarchical FADEC model integrates:
functional structure (block diagram);
physical structure;
Hierarchical state structure of power plant
\n\t\t\t\tFault levels\n\t\t\t | \n\t\t\t\n\t\t\t\tFaults\n\t\t\t | \n\t\t\t\n\t\t\t\tState\n\t\t\t | \n\t\t\t\n\t\t\t\tFault handling priority\n\t\t\t | \n\t\t||
Level 1 | \n\t\t\tFADEC fault | \n\t\t\tFault | \n\t\t\tImmediate | \n\t\t||
Fault of lane B | \n\t\t|||||
Levels 2-4 | \n\t\t\t\n\t\t\t | \n\t\t\t | Fault of control function (B) | \n\t\t\tDegradation | \n\t\t|
\n\t\t\t | \n\t\t\t | Fault of control loop (B) | \n\t\t|||
Levels 5-6 | \n\t\t\tIntegrated fault of measurement for alternative control law | \n\t\t\t\n\t\t\t | Fault of actuator control circuit (B) | \n\t\t||
Level 7 | \n\t\t\tFault of lane A | \n\t\t\tShort-term | \n\t\t|||
Level 8 | \n\t\t\t\n\t\t\t | \n\t\t\t | Fault of control function (А) | \n\t\t||
Fault of control loop | \n\t\t\t\n\t\t\t | Fault of control loop (А) | \n\t\t|||
Level 9 | \n\t\t\tIntegrated fault of measurement (fault of same measured parameters in channels A) | \n\t\t\t\n\t\t\t | Fault of actuator control circuit (А) (fault of communication lines of sensors or fault of FADEC hardware) | \n\t\t||
Level 10 | \n\t\t\tFault of measurement in channel (break or short circuit) | \n\t\t\t\n\t\t\t | \n\t\t\t | Long-term | \n\t\t|
\n\t\t\t | Sensors | \n\t\t\t\n\t\t\t | Actuators | \n\t\t\t\n\t\t\t | \n\t\t |
Fragment of hierarchical classification of faults for FADEC
tree states (state structure) of elements and units;
tree of failures influence indexes.
On the hierarchical model, the system of faults interference R with logical operations of a disjunction and a conjunction is applied. Such a system of faults interference allows to analyze the state of all power-plant, both from the bottom up to the top, and from the top down to the bottom and to carry out deeper analysis on various levels of decomposition of the control system using an intermediate state: degradation.
The degradation is understood as "package/complex of degradationary changes of the system" and the degradationary change is "a separately considered irreversible change of a structure of the system, worsening its properties, changing the parameters and characteristics".
Define the main faults of FADEC, the priorities of their elimination and the state they belong to. In Table 1, an exemplary of a fragment of the hierarchical classification of faults for sensors and actuators of FADEC is presented.
Fault levels 1 through 6 demand immediate handlings and correspond to the "catastrophic" and "critical" states by FAA classification (Federal Aviation Administration, U.S. Department of Transportation), given in [6]. The emerging of such states requires immediate landing of the aircraft. Fault levels 7 through 9 are classified as a "marginal" state and demand operative handling after landing. In this case, it is possible to continue the flight, but postflight repair on the ground is required. Faults at the 10th level demand their handling in long-term prospect.
The degradation process for FADEC starts at the 10th level of hierarchy. From the 4th level of hierarchy, the system starts to approach the system crash that can be regarded as «a critical situation».
Note that development of such faults in certain cases can be detected in advance by estimating the states of elements not only at the level of "0-1" (fully operational, operational/working, fault), but also by considering their gradual degradation.
The state of an element or a system is proposed to be represented in the form of three parameters { operational, degradation, fault }, see Tab. 2.
In the operational state S = 0, while during fault S = 1. The degradation degree range from "0" to "1". Thus, the extreme values "0" and "1" are defined according to the determined logic, which is realized in the conventional FADEC (according to the design specifications for the system). The introduction of this intermediate state of "degradation" expands the informativity of the conventional condition monitoring algorithms.
\n\t\t\t\tOperational\n\t\t\t | \n\t\t\t\n\t\t\t\tDegradation\n\t\t\t | \n\t\t\t\n\t\t\t\tFault\n\t\t\t | \n\t\t
\n\t\t\t\tS = 0 | \n\t\t\t0 < S < 1 | \n\t\t\t\n\t\t\t\tS = 1 | \n\t\t
Fuzzy representation of state
Based on the faults analysis and the hierarchy of states of the system at each level, the degree of degradation of each item or sub-unit is determined (Figure 3). Fault states are classified via degradation degree as "Negligible", "Marginal", "Critical" and "Catastrophic" [6]. The estimation of the degradation degree is defined on the membership function S which takes values in the range of
Estimation of "degradation" state
Consider an example of correspondence of degradation degree and the operational state. At the degradation degree of 0,25, the system is capable to carry out 75% of demanded functions (50% at 0,5 degradation, 25% at 0,75 and 0% at 1, which is the unavailable state). Such scale allows to define a “threshold” state, below which further operation is not allowed for safety reasons. Using the degradation degree, it is also possible to estimate the distance to a critical situation and the speed of approximation to it (Figure 4).
Trend of state dynamics during flight
Thus, the hierarchical model of fault developments allows to decompose the power-plant on hierarchy levels for obtaining quantitative estimates of the degradation state and gradual faults. The hierarchy analysis allows to utilize the state model and to estimate the system state at each level of hierarchy. The state is represented in the form of a vector with parameters { operational, degradation, fault }. Depending on the degradation degree it is possible to make an estimation of operability of object and system safety.
The built-in monitoring system (BMS) is a subsystem of monitoring, diagnosis and classification of faults of the gas turbine and its systems. The fault existence corresponds to a logic state of "1", the absence does to a logic state of "0". Such state classification doesn\'t allow to establish a "prefault" state, to trace faults’ development, and to define degradation of the system and its elements. For more detailed analysis, the estimation of the intermediate state of degradation is proposed. For this purpose, the use of fuzzy logic is considered. Signals from sensors, and also logic state parameters from BMS will transform to linguistic variables during fuzzification to a determined value arrives to the input of the fuzzifier. Let x is the state parameter of an element (for example, the sensor). It is necessary to define fuzzy spaces of input and output variables, and also terms for FADEC sensors. All signals from sensors and actuators will transform into linguistic variables by fuzzification.
Consider an example of fuzzy representation of a state of the two-channel sensor of rotational speed of a high pressure turbocompressor rotor. For monitoring of operational condition of communication lines of the FADEC sensor, a linguistic variable is introduced in the following way:
where Ωn is the sensor state,
xn is the number of events when n is beyond the allowed limit band;
B is { operational, fault };
U is [0,4]
G is the syntactic rule generating terms of set B,
M is the semantic rule, which to each linguistic value x associate with its sense of M(xn), and M(xn) designates a fuzzy subset of the carrier U.
Say that the sensor is considered failed after the fourth appearance of the shaft speed measurement beyond the allowed boundary, therefore the membership function is formed as shown in Figure 5.
At a single appearance out of limit (xn = 1), membership function B1 takes the value 0,7, and B2 = 0,3. The degradation degree takes the value of membership function B2. If the repeated breaking the limit doesn\'t prove to be true during the set period of time, the monitoring algorithm cancels the measurement: B1 = 1, B2 = 0.
Membership functions of "sensor state"
In Figure 5 two membership functions are shown: state B1 corresponds to the function μB1 (xn), B2 is described by the function μB2 (xn).
The way of creating fuzzy rules is presented in Figure 6. This rule base is represented by the table, which is filled in with fuzzy rules as follows [9]:
Example of fuzzy rule base
The values μT1, μT2, μT3 are set in the cell at the row “Operational" (A1) and the column "Fault" (B2).
Consider a fragment of the rule base for estimates of the sensor state of the low pressure shaft speed. Formulate the first rule: if the 1st coil (n11) of the sensor is operational (A1) and the 2nd coil (n12) of the sensor is operational (A1), then the sensor is in the operational condition with the following membership functions:
Write down this rule as follows:
Other rules are created in the similar way.
Given a greater number of possible conditions (for example, greater number of the duplicated coils of the sensor), one can develop a discrete-ordered scale of state parameters (Figure 7).
For further analysis of the system, enter the faults influence indexes at each level of hierarchy, using a method of pairwise comparison as it is carried out in the hierarchy analysis method.
Quantitative judgements on the importance of faults are performed for each pair of faults (Fi, Fj) and these are represented by matrix A of the n×n size.
where aij is the relative importance of fault Fi in regard to Fj. The value Aij defines the importance (respective values) Fi of faults in comparison with Fj.
Elements aij are defined by the following rules:
If aij =
If fault Fi has identical relative importance with Fj, then aij =1, aji=1, in particular aii=1 for all i.
Thus, a back-symmetric matrix A is obtained:
Continuous (a) and discrete (b) scale of degradation
After the representation of quantitative judgements about the fault pairs (Fi, Fj) in a numerical expression with the numbers aij, the problem is reduced to that n possible faults F1, F2, …, Fn will receive a corresponding set of numerical weights ω1, ω2, …, ωn, which would reflect the fixed judgements about the condition of the gas turbine subsystem.
If the expert judgement is absolute at all comparisons for all i, j, k, then matrix A is called consistent.
If the diagonal of matrix A consists of units (aij = 1) and A is the consistent matrix, then at small changes in aij the greatest eigenvalue λmax is close to n, and the other eigenvalue are close to zero.
Based on the matrix of pair comparison values of faults A, the vector of priorities for fault classification is obtained, along with vector ω satisfying the criterion:
where ω is the eigenvector of matrix A and λmax is the maximum eigenvalue, which is close to the matrix order n.
As it is desirable to have the normalized solution, let’s slightly change ω, considering
Note that small changes in aij cause small change in λmax, then the deviation of the latter from n is a coordination measure. It allows estimating proximity of the obtained scale to the basic scale of relations. Hence, the coordination index
is considered to be an indicator of "proximity to coordination". Generally, if this number is not greater than 0.1 then it is possible to be satisfied with the judgements about the faults importance.
At each level hi of the hierarchy for n elements of the gas turbine and its subsystems, the state vector {operational, degradation, fault} is determined, taking into account the influence coefficients of failures:
where
The output value
The state estimation begins with the bottom level of hierarchy. The description of a state set obtained by means of fuzzification and deffuzification with the use of the logic operations of disjunction
\n\t\t\t\t | \n\t\t\tlogic operation "AND" | \n\t\t
\n\t\t\t\t | \n\t\t\tlogic operation "OR" | \n\t\t
In performing operation "OR", the "worst" state vector is chosen, with the maximum parameters of degradation μdegradation(x) or faults μfault(x). The selector of maximum chooses from the fault influence indexes the one that has the maximum value.
The use of the hierarchical representation allows a small amount of "short" fuzzy rules to adequately describe multidimensional dependencies between inputs and outputs.
A promising approach to constructing intelligent systems of control, diagnosis and monitoring could be the stochastic modelling on the basis of Markov chains combined with the formalized hierarchy theory.
Within a fuzzy hierarchical model, consider fault development processes with the use of Markov chains. Such dynamic models allow to investigate the change of elements’ states in time. Fault development can include not only single faults and their combinations, but also sequences (chains) of so-called "consecutive" faults [10, 11].
During FADEC analysis, classification, formalization and representation of processes of condition monitoring and fault diagnosis for the main subsystems of gas turbines (control, monitoring, fuel supply etc.) is carried out. These processes are represented in the form of Markov chains which allow to analyze the state dynamics of the power-plant.
The transition probability matrix of a Markov chain for modeling faults and their consequences, has a universal structure for all levels of system decomposition (Figure 8):
system as a whole (power plant);
constructon units;
elements.
Hierarchical structure of Markov model of fault
The hierarchical Markov model is built in the generalized state space where physical parameters and binary fault flags are used for the estimation of a state vector of the element, unit and power-plant. The state vector includes three parameters { operational, degradation, fault } which allow to track the fault development and degradation process of the system. During FADEC diagnosis, the area of single faults is mostly considered. The proposed Markov model enables to present the system with multiple faults and their sequences. The top state level of a system reflects in the aggregated form the information on faults at the lower state levels.
The elements’ state at the levels of the hierarchy depends on the previous values of state parameters of the elements, values of membership functions and fault influence indexes.
For the estimation of transition probabilities between the states of a Markov chain, it is required to calculate relative frequencies of events such as
Transition probability matrix of power-plant during one flight
The most important events during flight are the emergency turning off of the engine stop (shutdown) and the possibility of its restart. For the probability estimation of such events, it is required to use statistics on all park of the same type engines. For realization of such estimation methods, it is required that flights information was available on each plane and power-plant. Such information should be gathered and stored in a uniform format and should be available for processing. Modern information technologies open possibilities for such research. To analyze fault development processes of one FADEC, it is possible to use results of the automated tests at the hardware-in-the-loop test bed with modeling of various faults and their combinations. In any case, to receive reliable statistical estimates one needs a representative sample of rather large amount of data.
In the analysis of the Markov model, the relation of the transition probability matrix with state of elements and subsystems at each level of hierarchy is considered. Therefore it is necessary to have the model of the system behavior in various states with various flight condition to guarantee system safety, reliable localization and accommodation of faults.
As the basic mathematical model of the controlled plant, the description in the state space is considered in the form of stochastic difference equations:
where
The level of quantisation allows the Markov process to be converted into the Markov chain. Provided ξ(t) is a stationary process, the Markov chain will be homogeneous. Such chain is described by the means of the stochastic transition probability matrix P with the dimensions (m×m), where m is the number of the chain states. Each element of the matrix Pij represents the probability of the system transition from the condition Хi into the condition Xj during the time interval ΔT:
Condition (2) means that the matrix P should be stochastic and define the full system of events. The sum of elements in each row of the stochastic matrix should equal 1.
The size of the matrix P is defined by the prior information on the order of the object model (1) and the number of the sampling intervals Δx and Δu. The transition probabilities are then estimated as relative frequencies of the corresponding discrete events.
The statistical estimation of the transition probabilities for the controlled Markov chain is performed as the calculation of the frequencies for the corresponding events during observation and the subsequent calculation of the elements of matrix P using the formula:
where the numerator Nijk is the number of the following events:
The normalisation of Equation (3) makes matrix P stochastic. As a result, the set of probabilities in each row Pij describes the full system of events for which the sum of probabilities is equal to unit:
The estimation of a transition probability matrix of the Markov model consists of creation of multidimensional histograms which represent an estimate of joint distribution [14, 15].
The use of the hierarchical Markov model allows to "compress" information which has been recorded during one flight, and to present it in a more compact form. In this case, the possibility of analysis and forecast of dynamics of degradation degree (Figure 10) opens. It is possible to analyze the state dynamics of elements and functions at each level of hierarchy in time for decision-making support.
Dynamics of state parameters during flight
The analysis of fault information and state change can be carried out over flight data for the whole duration of maintenance and the whole "fleet" of engines and their systems (Figure 11). Such analysis will assist to increase efficiency for processes of experimental maintenance development and monitoring system support.
Given statistics on all park of engines within several years, it is possible to build empirical estimates of probabilities of the first and second type errors.
Thus, possibilities of application of hierarchical Markov models for the gas turbine and its FADEC for compact representation of information on flight and for the assessment of "sensitivity" of the monitoring system according to actual data are considered. The levels of hierarchy differ with the ways of introducing redundancy and realization of system safety with use of intellectual algorithms of control and diagnosis. Each higher level of hierarchy has greater "intelligence" and is designed independently in the assumption of ideal system stability of the lower level.
Hierarchical fuzzy Markov model of gas turbine states
Consider an example. In Figure 12, the FADEC state estimation with faults is presented on the basis of the degradation degree of the elements.
Example of hierarchical estimation of state parameters
At the 10th level the BMS detected a fault of measurement in the form of break of the first coil of parameter n11 (shaft speed sensor). On the basis of the fuzzy rule R(2), the parameters of measurement state of n1 in the channel A are characterized by the following three values
The measurement state in the channel B is defined as
The state of an element of a higher level is calculated by multiplication of the current state to fault influence indexes of the fault elements. The state of both measurement channels of temperature T is "operational", therefore, the sensor T state equals
At the 8th level the state of two sensors n and T after similar calculations becomes equal { 0,78; 0,19; 0,03 } that indicated the system degradation in the part of control of fuel consumption.
For the estimation of a state of the fuel consumption control function, the "OR" operation is also used. The state of the actuator of fuel consumption control circuit is characterized by the parameters { 0,66; 0,34; 0 }. The state of FADEC is characterized by the fault of fuel consumption control function or the state of the guide vanes control function. Using the operation "OR", the state of FADEC is detected as { 0,66; 0,34; 0 }. In this example, the whole system is considered to be operational, whereas partial degradation is observed, which is not influencing the system operability.
Thus, the technique of state parameters determination for FADEC and its systems on the basis of fuzzy logic and Markov chains is proposed. This technique can be used during flight or in maintenance on the ground.
At the present time a necessary condition for realization of intellectual algorithms is the complete development of the distributed intellectual control models focused on control optimization, forecasting and system safety [16, 17]. In Figure 13, the scheme of the distributed FADEC is shown.
Thus, in each sensor or actuator, it is necessary to have physically built-in control system (or function) to form and monitor the fault signals in the unit, communication lines, cooperating sensors, indication devices and systems [18]. The use of the built-in monitoring control systems working in real time allows to obtain a number of additional possibilities for improving control quality and system operational characteristics as followings:
emergency states detection of the control object and system;
fault detection of elements of the control object;
state diagnosis and parametrical degradation of the object.
Distributed architecture of FADEC
In this chapter, the hierarchical fuzzy Markov modeling of fault developments processes has been proposed for the analysis of an airplane system safety. The hierarchical model integrates functional, physical structure of gas turbine and its FADEC elements and units, the tree of states, a tree of fault influence indexes. This model allows to decompose the power-plant for a quantitative estimates of degradation state and gradual faults. The analysis of hierarchies allows to utilize the state model on the basis of fault development processes which estimates the power-plant state at each level of hierarchy. Furthermore, the technique of determination of state parameters of the gas turbine and its systems on the basis of fuzzy logic is presented. The state of each element, unit and system is represented in the form of a vector with parameters { operational, degradation, faults }. The use of the proposed indicator ”degradation degree” allows to obtain an objective quantitative estimate of the current state which can be used as, the "distance" to a critical situation and the reserve of time for decision-making in-flight. This indicator is defined on the basis of the discrete-ordered scale and fault influence indexes that allows to determine about 30 % of gradual faults in gas turbine and its systems at the stage of fault development. The examples of fuzzy rules on the basis of expert knowledge are given, whereas fuzzy logic is used for interpolation.
The application of hierarchical Markov models for the analysis of experimental data is also considered for control system development: as the compact representation of information of a system state change during flight, the estimation of transition probabilities.
FADEC – Full Authority Digital Engine Control.
BMS – built-in monitoring system.
ω – weighting coefficient of fault.
S – state (condition) of gas turbines.
µ – membership function.
P – probability.
X – state variable.
U – control variable.
Nuclear medicine involves use of radioactive atoms for diagnosis and/or therapy. For therapeutic purposes, to obtain specific irradiation of tumor cells, radioactivity is attached to a pharmaceutical molecule that binds to specific molecules expressed on the target tumor cells. This specific radioactive molecule is known as radiopharmaceutical. The pharmacological specific component of a therapeutic radiopharmaceutical can be based on the target protein structure which may include peptides or monoclonal antibodies, or molecular structures like nanoparticles [1]. The radioactive part may consist of massive particle emitters capable of delivering ionizing energy locally as Auger electrons, or β− or α particles. Auger electrons are low-energy electrons that emit localized irradiation, few nanometers around the emission point with high biological effects. Beta-negative particles have a comparatively low linear energy transfer (LET) and emit their energy over a few millimeters in comparison to alpha particles. The choice of the radionuclide is based upon the size of the tumor. For example, yttrium-90 emits a long-range beta emission and could be useful for proliferating tumors of large size, while lutetium-177 having a short range emission could be used for treatment retreating tumors of small size. Alpha particles deliver a high fraction of their energy inside the targeted cells, leading to highly efficient killing. This makes them suitable for targeting cells of isolated tumor and minimal residual disease [2, 3].
Radioimmunotherapy, radiopeptide therapy and radionanoparticles are three important strategies of nuclear medicine for glioblastoma therapy. The four main prerequisites for successful radionuclide therapy for glioblastoma are selection of an appropriate target (integrin, tenascin, cadherin, EGFR, chemokine receptors or neurokinin receptors), physicochemical properties of the radionuclide, physicochemical properties of the targeting vector and its size [4]. For therapeutic purposes, nuclear medicine practitioners typically use β− particle emitters like 131I, 90Y, 186/188Re, and 177Lu. These radioisotopes have been coupled with nanoparticles, monoclonal antibodies, or peptides for treatment of glioblastoma. These radiopharmaceuticals have resulted in maintenance and/or improvement of the neurological status with only short-term side effects. The evidence for glioblastoma targeted radiotherapy has not only proven for β− particle emitters but also for α particle emitters. 213Bi, 211At, and 225Ac are some of the particle emitters which are recently attracting the interest of the scientific community. They are capable of delivering high amount of their energy within few micrometers close to their emission point in comparison to some few millimeters for β− particles. The α particles have been found highly efficient in killing tumor cells with minimal irradiation of healthy tissues and permits targeting of isolated tumor cells [1, 5].
Gliomas are the most frequent, very diverse group of intrinsic tumors of the central nervous system and are conventionally classified in harmony to their microscopic alikeness with recognized cells of origin according to glial precursor cell families. Major groups consist of diffuse gliomas, categorized by widespread growth into the adjoining CNS parenchyma, and more confined “nondiffuse” gliomas, with pilocytic astrocytoma and ependymomas [6]. The fourth edition of the WHO Classification of CNS tumors published in 2016 has essentially changed the classification of diffuse gliomas. These tumors are presently defined based on presence/absence of IDH mutation and 1p/19q codeletion. It can be attributed to massive expansion of knowledge on molecular alterations in tumors of the central nervous system (CNS) [2]. Until now, tumors were defined based on their histology. Any molecular information was mainly provided as supplementary information within histologically defined categories. Current advances in the molecular conceptualization of gliomas recommend some probable reasons for the failure of targeted therapies in gliomas. Specially, the histologic-based glioma categorization comprises of multiple molecular subtypes with discrete biology, usual history, and diagnosis. These observations have resulted in improvement in diagnosis and classification by the World Health Organization [7]. These perceptions regarding glioma biomarkers and subtypes highlight several clinical challenges. Firstly, the field is witnessing the struggle of reconsidering the results of previous studies and retrospective data using the new classifications to explain prognostic assessments and treatment recommendations for patients. Secondly, the new classification requires changes in the design and stratification of future clinical trials. Hence, these observations offer the required framework for the growth and evaluation of novel targeted therapies for specific glioma subtypes [2, 8].
Drug delivery to tumor can be monitored using nuclear medicine imaging techniques like single-photon emission computed tomography (SPECT), positron-emission-tomography (PET). In single-photon emission computed tomography (SPECT), a gamma-emitting tracer allows for three dimensional visualization of the drug [9]. The radioisotope is either administered with the drug or directly bound to the biologically active molecule such as siRNA, so that their volume of distribution can be determined easily. Accurate anatomic estimates can be obtained by combining SPECT with CT or MRI. This approach is less expensive in comparison to other nuclear medicine imaging modalities [10]. Conventional SPECT suffer from poor limitation. However, recent advances involving pinhole-SPECT has improved the resolution to millimeter level [11].
Another promising modality for imaging drug delivery to tumor is positron-emission-tomography (PET). PET tracers are administered with the drug or are bound to the carrier like nanoparticles [12]. The PET scan can be correlated with CT scan in order to determine path of diffusion of tracer.
Similar to gadolinium and SPECT contrast agents, PET tracers can be infused concurrently with drug or bound to the delivery system, such as nanoparticles [12]. When PET is coupled with CT, molecular movement can be correlated with anatomy, with measurement of area of diffusion of tracer or tracer-incorporated carrier. PET imaging can estimate the borders of a tumor through the use of tracers that are derivatives of amino acid such as O-(2-[18F]fluoroethyl)-L-tyrosine (FET) thus allowing precise assessment of drug distribution relative to tumor volume than MRI [13]. Limitations of PET and SPECT imaging include radiation exposure, the high cost, and short-lived nature of PET tracers. Another important limitation similar to gadolinium agents in MRI is the tracer has to directly couple to the delivery agent; otherwise the measurement of the area of diffusion is indirect. These limitations can be overcome by direct radiolabeling of nanoparticles [14].
Drug delivery across the BBB requires knowledge of both “barrier” and permeability properties of the brain endothelial cells. Transport across BBB may involve simple diffusion, facilitated diffusion, diffusion through aqueous pores, and active transport through protein carriers. In case of simple diffusion solute molecules travels along concentration gradient. Facilitated diffusion involves binding with specific membrane-traversing protein, coupled with movement along the concentration gradient. Charged ions and solutes cross the BBB by diffusion through aqueous pores. Active transport of solutes through protein carrier against concentration gradient involves expenditure of ATP. The presence of large number of mitochondria in the endothelial cells is thought to provide the required energy in form of ATP [15]. This mechanism involves an alteration in the affinity of a carrier for the solute molecules as it travels across the BBB. While designing nanocarrier mediated CNS delivery, transporter systems involved in ferrying essential molecules such as glucose are of utmost importance. These systems can be employed for delivery of potential nanotheranostics across the BBB. There are five types of sodium-independent glucose transporters (GLUT) which transport 2-deoxyglucose, 3-O-methylglucose, mannose, galactose and glucose across the BBB. The most important being 45–55 kDa glycosylated protein GLUT-1. It is mostly present in endothelial cells of arterioles, venules and capillaries, wherein it facilitates movement of D-glucose from the peripheral circulation into the brain. Other worth mentioning glucose transporters are GLUT-3 in brain neurons and GLUT-5 in microglial cells in the brain. They transport 2-deoxyglucose, 3-O-methylglucose, mannose, galactose and glucose across the BBB [16].
Another significant transport system that works in an analogous manner is P-glycoprotein multiple drug resistant protein (P-gp, MDR1). It has been comprehensively investigated as a possible carrier for drug delivery. This efflux transporter is usually expressed on luminal surface of endothelial cells, astrocytes and microglial cells. It prevents toxins from gaining entry into the brain parenchyma [17, 18]. Anticancer agents like Vinca alkaloids, anthracyclines, and taxanes are substrates for MDR1 are transported by Pgp. It limits their accumulation in the brain. Recently, it has been found that MDR1 regulation is altered by various disease conditions, and, in turn, diseases of the brain influence MDR1 expression [19, 20]. The presence of large number of receptors at the surface of BBB can be utilized by potential nanocarriers for enhanced brain by coupling with receptor-specific molecules or analogues. A large number of molecules such as insulin, insulin-like growth factors (IGF-1 and IGF-2), leptins, and transferrin can be transported into the brain following receptor-mediated endocytosis [13]. The nanoparticles should be designed to bypass efflux transport systems present at the luminal side (such as MDR1). Instead, nanoparticles could be substrates of transport mechanisms enhancing the passage of specific molecules like GLUT-1, IGF-1, and IGF-2 across the BBB [21].
The WHO 2016 Classification of gliomas represents a paradigm shift as; for the first time, the definition of many of these neoplasms is partly based on genetic characteristics based on molecular markers. This was a major step forward toward a more precise diagnosis of gliomas and will in the course of time certainly facilitate improved therapeutic management of the patients suffering from these tumors. Diffuse gliomas are the most common intrinsic CNS neoplasms, found in adults. On the basis of histopathological analysis, these gliomas were conventionally diagnosed as diffuse astrocytomas (with glioblastoma as it is most common and malignant representative), oligodendrogliomas, or as tumors with a mixed astrocytic and oligodendroglial phenotype (oligoastrocytomas) [6]. Within these subgroups, a malignancy grade (WHO grade II, III or IV) was assigned based on the presence/absence of marked mitotic activity, necrosis and florid microvascular proliferation. The major change can be attributed to use of isocitrate dehydrogenase (IDH mutation) as a marker in diffuse glioma classification. The categorization of diffuse gliomas on the basis of genotype involves high incidence of point mutations in isocitrate dehydrogenase 1 and 2 (IDH1/IDH2) in WHO grade II and III astrocytomas, oligodendrogliomas, oligoastrocytomas and secondary glioblastomas. Lower grade neoplasms usually develop into secondary glioblastomas [8]. Hence, it became clear that tumors with identical histology can lead to different clinical outcome such as IDH-wildtype and IDH-mutant diffuse gliomas. Many histologically similar WHO grade II and WHO grade III IDH-wild type diffuse gliomas exhibit molecular characteristics like glioblastoma. These facts ultimately led to inclusion of IDH mutation as a crucial marker for glioma classification and the introduction of, genetically defined entities: diffuse astrocytoma, IDH-mutant; anaplastic astrocytoma, IDH-mutant; oligodendroglioma, IDH-mutant; anaplastic oligodendroglioma, IDH-mutant; and glioblastoma, IDH-mutant [7]. The molecular features of IDH-mutant glioma outweigh the histological diagnosis. A tumor having histology of an astrocytoma, detection of complete 1p/19q codeletion leads to diagnosis of oligodendroglioma. Likewise, for diffuse, IDH-mutant gliomas with oligodendroglial phenotype with complete absence of 1p/19q codeletion, the collective diagnosis may be astrocytoma, IDH-mutant and 1p/19q-non-codeleted [8]. Based on IDH mutation status, glioblastomas were reclassified as glioblastoma, IDH-wildtype and glioblastoma, IDH-mutant. This latter category largely overlaps with what previously described secondary glioblastoma based on clinical, radiological and/or pathological evidence of a lower grade precursor lesion. Patients with a secondary glioblastoma or IDH-mutant glioblastoma are normally younger and have improved diagnosis than those with glioblastoma, IDH-wildtype. Analogous to grade II and grade III oligoastrocytic tumors, most glioblastomas with oligodendroglioma as explained in the WHO 2016 Classification are part of one of the genetic subgroups of diffuse glioma [7, 8]. One of the treatment strategies which are catching the attention of oncologist is nanotechnology. Nanoparticles (NP) are entities possessing diameter of 10–200 nm that hold great possibilities for design and biological applications. There has been an upsurge in development of nanodevices for diagnosis and treatment of brain tumors. Nanoparticles are carriers that can be designed to ferry one or more types of molecules to brain including MRI contrast agents, fluorescent and visible dyes, chemotherapeutic agents and photosensitizers. The targeted delivery of nanoparticles to brain tumors can be augmented by altering their particle size and surface characteristics [22, 23].
There has been moderate impact of targeted therapies in glioma. The therapies that have demonstrated a significant survival benefit for gliomas in Phase III clinical trials, including radiation, chemotherapy (temozolomide and PCV [procarbazine, lomustine, vincristine]), and tumor-treating fields, are based on nonspecific targeting of proliferating cells. An emerging field in glioblastoma nuclear medicine is use of radionanoparticles. These radioactive nanocarriers can be used passively as a simple tumor brachytherapy or can be actively used with a specific targeting to vectorize a large amount of radioactivity. The targeting is usually directed against a glioblastoma-specific antigen or receptor. Antigen targets, like epidermal growth factor receptor (EGFR), tenascin, or DNA histone H1 complex. Radiolabeled antibodies and peptides hold promise for molecular radiotherapy but are often limited by a low payload resulting in inadequate delivery of radioactivity to tumor tissue and, therefore, inadequate therapeutic effect and adverse effects due irradiation of normal tissues [24]. Song et al. developed a synthetic method of radiolabeling indium-111 (111In) to epidermal growth factor (EGF)-gold nanoparticles (111In-EGF-Au NP) with a high payload [25]. By using radiolabeled nanoparticles, comparatively higher payloads are obtained due to large surface area to volume ratio. This results in multivalent effect of nanoparticles, thus accommodating a large number of targeting ligands, such as antibodies, peptides or aptamers on a single nanoparticle. This facilitates maximal binding to the molecular target in vivo, thus enhancing delivery of radioactivity to target tissue with improved imaging and therapeutic efficacy. PEGylation of nanoparticles and alteration of their surface properties improves their stability and mean residence time in vivo [26]. It also permits loading a combination of imaging, radiotherapeutic and/or chemotherapeutic moieties for multimodal tumor imaging and therapy [27]. Antibodies, radiolabeled antibodies, antibody fragments or peptides because of their small size easily penetrate surrounding normal tissues. Loading onto nanoparticles limits their penetration through normal vasculature and capillaries, thus minimizing their side-effects [28].
Different nanocarriers such as metallofullerenes, liposomes, or lipid nanocapsules have been used to deliver radionanoparticle passively. A typical metallofullerene (177Lu-DOTA-f-Gd3N@C80) radionanoparticles when administered by convection-enhanced delivery (CED) in brain tumor model showed an improved survival time of more than 2.5 times that of the control group [29]. Similarly liposomes loaded with beta-negative emitters such rhenium-186 and demonstrated promising results when administered by CED in an orthotopic glioblastoma rat model [30]. Lipid nanocapsules loaded with rhenium-188 in a rat orthotopic model showed a significant survival benefit after intratumoral stereotactic injection at day 6 and CED injection at day 12 [31].
A recent approach using radionanoparticles consists of an active targeting approach where the nanoparticles are functionalized and directed against a tumor target. The aim of this active targeting is to optimize the spatial localization of the radioactivity close to the tumor cells. As an example, lipid nanocapsules can be loaded with rhenium-188 and coupled to a monoclonal antibody directed against the CXCR4 antigen. These CXCR4-recognizing immune-nanoparticles irradiate the tumor cells and have been shown to increase efficacy in an orthotopic mouse model. Recurrence for the passive protocol was observed at 65 versus 100 days for the active targeting approach, and this appears to be the most effective therapy with the longest measured time to progression [32].
Neural stem cells (NSCs) are increasingly being used as carriers for targeted delivery of therapeutics to glioblastoma. This requires multimodal dynamic in vivo imaging of NSC in the brain. Such type of technology is in development phase. Cheng et al. reported an innovative strategy for neural stem cell tracking in brain using silica nanoparticles via SPECT [33]. 111In radioisotopes were conjugated to porous silica nanoparticles having large surface area. A series of nanomaterial characterization assays were performed to evaluate the modified mesoporous silica nanoparticles. Loading efficiency and viability of NSCs with 111In-MSN complex was validated. Radiolabeled NSCs were administered to glioma-bearing mice via intracranial or systemic injection. SPECT and bioluminescence imaging were performed periodically after NSC injection. Histology and immunocytochemistry were performed to endorse the findings. 111In-MSN complexes showed minimal toxicity to NSCs and adequate in vitro and in vivo stability. Phantom studies establish possibility of mesoporous silica nanoparticles for NSC imaging. It was found that decayed 111In-MSN complexes exhibited significant fluorescent profiles in preloaded NSCs, thus validating ex vivo data. In vivo, SPECT images reveal actively migrating NSCs toward glioma xenografts in real time after both intracranial and systemic injection. This is in consonance with findings of histology, confocal microscopy and bioluminescence live imaging [33].
An urgent requirement for rapid detection and diagnosis of diseases has led to development of contrast agents and imaging techniques. The present challenge is for fast and complete imaging of tissues and lesion categorization that could be obtained by development of nontoxic contrast agents with longer blood circulation time. Nanotechnology provides apt solution to this problem. Nanoparticle based contrast agents have been employed in most biomedical imaging techniques like MRI, fluorescence imaging, CT, ultrasound, PET and SPECT. However, these imaging techniques have certain limitations. These can be overcome by use of multifunctional nanoplatforms to enhance safety, efficacy and theranostic attributes. The WHO 2016 Classification is a major step forward toward a more precise diagnosis of gliomas and will in the course of time certainly facilitate improved therapeutic management of the patients suffering from these tumors. The paradigm shift is IDH mutation as a marker in diffuse glioma classification and reclassification of glioblastoma. Novel drug delivery approaches have substantially influenced the glioblastoma treatment. There is urgent requirement of smart delivery systems for future therapies targeted to specific cells, dependent on intracellular delivery of agents impermeable to BBB. Polymer implants, convection enhanced delivery and degradable nanoparticles are some of the platform technologies for design of novel methods for treatment of glioblastoma. One strategy to optimize the efficacy of molecularly targeted radionuclide agents is to develop nanoparticle-based targeted delivery systems. An abundance of receptors at the surface of the BBB can be utilized by nanoparticles for enhanced brain uptake by coupling with receptor-specific molecules or analogues. The nanoparticles should be designed to bypass efflux transport systems present at the luminal side (such as MDR1). Instead, nanoparticles could be substrates of transport mechanisms enhancing the passage of specific molecules like GLUT-1, IGF-1, and IGF-2 across the BBB. Radiolabelled nanoparticles seem to be novel promising arsenal for potential neurotheranostics.
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