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1. Introduction
Today, as in yesterday, in the world not all societies benefit from the available resources at the same rate. The population and problems of the world are increasing in the distribution of resources. The population of the world, which was 2.5 billion in 1950, now exceeds 7 billion and in 2050, it is estimated to be 9 billion. In areas, such as education and health, global solutions are being tried to be produced under the roof of the United Nations (UN). In year 2015, member countries adopted a set of goals to end poverty, protect the planet, and ensure prosperity for all the new sustainable development agenda. According to the UN, new agenda has 17 sustainable development goals for sustainable development which cannot be realized without peace and security. Peace and security will be at risk without sustainable development [1]. There are currently around 750 million people worldwide who are illiterate. Underdevelopment is a tremendous problem, especially among low-developed and developing countries, under the influence of uneducated and old technology. This is more evident in the rural areas and in the agricultural sector.
From the most primitive to the most advanced, every society is in a constant social change. The direction of change is forward, and it is essential to establish a flawless society system. Social change from socioeconomic perspective is to move society from the current situation of production stage to a higher stage. Very rapid social change is causing crises. However, the slow pace of change also reveals some important social and economic problems [2]. As it is known, increasing population and production are the basic dynamics of social change. These dynamics change with the effect. Increasing production is possible only through the technological applications. The new technologies developed in the rural and urban area, the field of application is quite flourishing, the momentum of social change. However, every society changes under the influence of socioeconomic factors. This shows that each society is at certain stages of development due to the characteristics they possess. According to a classification, some societies in the world today bear the characteristics of an agrarian society, some industrial societies, and a small part of the information society. In addition, some societies live the transition period. These societies live under different peculiar conditions, with different forms of production and different production relations. And today we are talking about the revolution in industry 4.0 and the features of this period. These developments affect all sectors whatever the process, there is a great need for urban and rural extension, in addition to formal education, in order to regulate production and consumption relations, especially in the transitional societies.
2. Education and extension for development
Studies on education compliance with changing conditions or human resources and factors affecting country’s development were started in the eighteenth century with first economist who revealed the idea of classical economics and this process was improved with work by other scientist. According to this, the first attention to the importance of investing in human skills in economic development has been the pioneers of Adam Smith and classical economics. Schultz (1961) and Denison (1962) have shown that education contributes directly to the growth of national income through the development of the capacity and capacity of the workforce. In a study, Denison (1962) found that 23% of the growth in the US production between 1930 and 1960 was due to the increase in the level of education of the workforce. In another study by Denison (1967), 15% of the economic growth was found due to an increase in the level of education. This rate is 12% in the UK, 7% in Italy, and 2% in Germany from other industrialized countries. Canada ranks first among the countries covered by 25%. The same approach has been applied in some other countries. The results are as follows: 23.2% in Ghana, 16% in Nigeria, 16.5% in Argentina, 6.5% in Honduras, and 3.3% in Brazil. These results in both developed and developing countries, since the 1950s, “a significant part of the growth in output can be explained by the increase in the level of training of the workforce.” However, it should be noted that these estimates are based on various theoretical assumptions. In the following years, many studies were carried out. Again, there is a relationship between education and output growth. In a study by Hicks (1980), in 83 developing countries between 1960 and 1977, the relationship between literacy, life expectancy, and growth as a measure of cooperative development was examined. The findings showed that literacy rates and life expectancy in the 12 countries in which the fastest growth occurred were well above average values [3].
In 1980, in the light of findings revealed the contribution of education to human resources, and economic development was approved. Especially, the importance of education, ensuring the development of human resources in country’s development, has begun to be considered as a concrete. Besides the impact of education on the economic development, significant advantages of compliance of society with new conditions have been proved.
In this context, from 1993 to now, every year studies are carried out by United Nation Development Program (UNDP) on the development of nations and presented in report form. In the report, values of human development index (HDI), health conditions of a country, education level, and human development (HD) calculated from national income allow us to make assessment on the availability of living conditions in the country. For example, according to 2015 human development report, Norway was the best country to live, whereas the Niger was the worst country to live in.
3. Human development and research and development (R&D)
Progress is evident for many measures of human development such as those for health, education, income, security, and participation and for such composite indicators as the human development index (HDI) [4]. According to this, human development (HD) is about expanding the richness of human life rather than simply the richness of the economy in which people live. HD grew out of global discussions on the links between economic growth and development [4]. In terms of showing human well-being, the HD value is an important indicator. Research and development (R&D) is very important for human potential and resource utilization efficiency. Therefore, the R&D capacity of country is important for human development (HD) of that country. The HD index provides insights into the development of countries.
There has been seen significant improvement in the HDI value for world generally and least developed countries particularly. During past 25 years, the HDI value for world increases by more than 20–40% for the least developed countries. There has been gain in HDI by every region of the world. During the past 15 years, although at slower pace, the progress in HDI has been fairly steady across all developing countries, most of the countries have moved up from previous position in human development classification [4].
Up to 2009 human development report, countries were classified in high, medium, and low development groups, whereas from 2010, it was started to classify countries in very high, high, medium, and low development groups. Norway, Australia, New Zeeland, and America were ranked at top in 2010 by human development report, whereas Zimbabwe was found at last. In 2010 report in addition to human development index, new three indexes were added, which provide new opportunities to evaluate human development from different aspects. These were expressed as the human development index modified for inequality, gender inequality index, and multidimensional poverty index [5, 6]. Norway, Australia, Switzerland, and Denmark are ranked at top in 2015 by human development report, whereas Niger is found at last. In these conditions, Norway is found to be best country in the world from living point of view. Turkey is holding 72nd position among 188 countries in the list. Despite the effort on demonstration of data aimed at ensuring the assessment on development, index values received by the countries are inadequate to explain the economic size and their place in human development. Hence, in this regard, it is impossible to find exactly something in response to some important questions.
For example, according to facts given by OECD, Turkey is the 17th big economy of the world. Question is being 17th biggest economy of the world, what position Turkey hold in human development? According to United Nation Development Program’s Human Development Assessment, Turkey is at the 72nd position. When considering the difference of 55th ranking between the evaluation result of UNDP and OECD, at first glance it may seem interesting and, there is a need for some interpretation and analysis [7].
Still having better values of index than Turkey, located in upper ranks in terms of human development countries such as Albania, Bulgaria, Tunisia, and Jordan; again countries such as Qatar and Bahrain found in very high development group with countries found in high development group such as Kuwait, Libya, Saudi Arabia, Iran, Brazil, and Tunisia, when comparing the current political, economic, and social conditions of above-mentioned countries with Turkey’s condition, Turkey’s position in human development at 72nd can be very difficult to explain. Especially mentioned countries having very low standard of institutes and politics, it is necessary to consider their high natural resources and wealth effect on their presence at top. In this case, at least the effect of natural resources on economic wealth reducing the impact of all other important factors, the issues such as equitable sharing of wealth effects on index also need to be focused.
The following result can be drawn from the example given above:
Just moving with economic size does not reflect the development.
In terms of human development besides national income indicators such as education standard of living is also important.
In revealing the state of development of a country, only economic size (growth) is not enough; with this, importance of indicators like human development and standard of living should be taken into account in analysis.
Economic growth does not affect the human development at the same rate.
HDI parameter also does not reflect the full rank of human development of a nation.
Essentially for the reasons stated above, there should be debate on national and international levels on the concept of development. In the same contest, the method for calculating IGE, considering the different dimensions of development indicator, up to now is continued to be in the developing process.
R&D is an expensive and long-term process, and it is clear how important it is. There are a variety of indicators which show state of preference or interest a country has shown in R&D. These are shown in Table 1 and figures as follows:
Human development groups
Human development index (HDI) (2014)
Research and dev. expenditure (%of GDP) (2005–2012)
Gross domestic product (GDP) (2011 ppp $ billion)
Life expectancy (2014)
Per capita (2014 ppp $ billion)
Public expenditure on education (% of GDP, 2005–2014)
Standard of living (% satisfied 2014)
Employed in agriculture (2012)
Employed in services (2012)
Labor productivity (output per worker) (ppp) (2005–2012)
Research and development expenditure by country groups.
Figure 2.
Public expenditure on education by country groups (% of GDP, 2005–2014).
Figure 3.
Per capita (GDP) by country groups (2014, PPP$).
Figure 4.
Standard of living by country groups (% satisfied).
Figure 5.
Life expectancy by country groups (2014).
Figure 6.
Human development index (HDI) by country groups (2014).
As shown in Table 1 and Figure 7, R&D activities are examined by country groups during the period 2005–2012. According to this, in the high development group, R&D expenditures are highest (2.4% of GDP). Then, the high human development group (1.4%) followed by the medium (1.4%) and low groups (0.5%) are listed. It can be seen that as the level of economic development of the country increases, the share allocated to R&D is also increasing. This suggests that countries in the medium and low groups, especially those in need of redevelopment, should allocate more resources to R&D.
Figure 7.
Selected some important indicators by country groups.
The current development is measured by only quantitative data on national income, with national income the average life expectancy of population, education level, and living standard of society will be more meaningful if tried to use in the calculation method to explain development. In this respect, focusing on the development of past 40 years, living conditions in Turkey, Norway, Australia, and Oman are improved by 45, 17, 21, and 1.2%, respectively, and living conditions have been exacerbated in Zimbabwe (27%). As a result, the fact that the some aspects of human development index should be discussed. As a sample, Turkey is in the high development group for human development. Position at the last rows in the group of countries shows that there are many things to be done for the some other countries.
Consequently to increase the prosperity and development of the country, everyone who cares about this has a very big responsibility. For adaptation of this task by community as a mission and in a systematic way, national education policy should be sensitive in this issue and continuity and enforcement is needed. Time is needed for this cause, but from today, anyone who is part of this community in order to contribute to this area should start “doing his job right and doing job by giving his due right.” By developing our sensitivity in this direction, we can sustain this idea by contributing effort in our living and working environment.
4. A research and development (R&D) equipped with extension
The level of societal development is closely related to right planning of human resources and research and development policy. Well-equipped human resource is very important for research and development (R&D), and R & D has vital importance from resource development and efficient use of resources perspective. Research and development studies are essential for the future of countries. The first goal of the research must be pure science, for the future of humanity. However, the research should be aimed at changing the current situation, improving it, solving problems, and developing new technologies. The delivery of technologies developed in the second stage to the target groups should be the adoption of innovations. Countries get benefits from findings of the science in order to make their development sustainable and move ahead in this race. This is undoubtedly true for every sector. Animal and crop production can be given as an example to see concrete results. Even in terms of measurability of change, animal and crop production branches of science have important advantages. The reason behind countries with high agricultural potential cannot show the expected performance in agriculture is: inadequacies in human resource planning, lack of R&D, and failure to develop technology production capacity, along with ineffective operation of research and extension. Like in every sector, the technology that is produced cannot be delivered to target masses with the right method is big problem and need urgent importance. According to studies, in addition to R&D, R&E should also be considered important. If the findings do not arrive to target groups, sector, and stakeholders, it is not possible to get positive points from the “impact analysis” of the conducted research. For this reason, both research and development (R&D) and extension are important and should go parallel. Sufficient and adequate budget should be allocated to both.
4.1. Research and development policy
It is very clear that science and technology should be supported with long-term policy. Especially, in ensuring economic and social development, science and technology policies offer significant advantages in determining pace and direction of development.
Agriculture started to witness technological changes when the cultivators first experimented growing wild plants under different growing environment almost 10,000 years ago. For centuries, the technical performance of agriculture more or less remained the same in the great civilizations. Until middle of the nineteenth century, there were not any significant improvements in agricultural productivity. In the nineteenth century, induction of new sources of power and new machinery [8], development of scientific plant breeding led by Mendel’s experiment, and development of artificial fertilizers resulted rapid increase in agriculture productivity, principally in Europe and North America.
Research and development often abbreviated to R&D covers all creative work undertaken systematically with a view to increase the sum of knowledge, including knowledge of man, culture, and the company, as well as the use of this sum of knowledge for new applications. In general, the term R&D covers three activities: basic research, applied research, and/or experimental development [9].
In 1960, Turkey switched to the planned development process. With that, the work on formulation of science and technology policies is continuing till date. During this process, Turkish science policy “Turkish Science and Technology Policy Draft (1993–2003)” has been put forward. However, although the documents contain important issues, by not fully putting in practice legal and institutional amendments made it difficult to reach expected results. Later on, work has been done to formulate a country vision on science and technology commonly accepted by all stakeholders (political institutions, public, private sector, and universities). As a result, “vision 2023: science and technology strategies project” entered implementation phase. Following activities are covered by project, assessing where Turkey stands in the field of science and technology, analysis of scientific and technological developments carried out in world with long-term objectives, acquirement of strategic technologies to achieve said targets, and formulation of policies aimed at developing or acquiring said technologies. According to decision of the supreme council of science and technology, in coordination with other related institutions and establishment, the task of implementation of the project has been assigned to the Scientific and Research Council of Turkey (TUBITAK). Turkey’s R&D policies are based on TUBITAK Vision 2023. Technology fields mentioned in this document (production, biotechnology, food processing, information, protection, diagnosis and treatment, storage and packaging, analysis and measurement, mechanization, and transport) are very closely related to agriculture. Technological activities related to R&D in food and agriculture sector (development of new genotypes seed with combination of classic breeding and technology; production of seed and seedlings; characterization and preservation of genetic resources; variety of processed products and food production methods and processes; food safety and credibility; development of production system in agriculture, forest, food, and fisheries products with help of infrastructure, tools, and equipments; combating disease pandemics with protection, control, and treatment techniques and enabling integrated combating; evaluation and development of natural resources and wildlife; and development and dissemination of remote sensing, early warning system, and information technologies in agriculture and forestry) are related to animal and crops production sector. This draft aims to make Turkey an advance country depending on science and modern technology, by healthy nutrition of society, ability to meet requirement in sufficient quantity and quality, protecting biological diversity and transforming into social welfare, economically, socially, and ecological sustainable, increasing productivity, with help of agriculture and agricultural industry. The Prime Ministry National Science, Technology and Innovation Strategy document [10] makes important description to achieve above-mentioned developments. Attention is drawn toward issues that will increase the functionality of the basic dynamic in the R&D and innovation system; in this way, goal is to achieve sustainability by accelerating the speed of development in R&D and innovation capacity. Again, the development of human resources, encouraging the transformation of research results into commercial products and services, promoting multi-partner and multi-disciplinary R&D and innovation cooperation culture, encouraging SMEs to be stronger actors in the innovation system, increasing the contribution of research infrastructures to Turkey’s research field (TARAL), and enabling international cooperation in the development of the science, technology, and innovation strategy, is stressed in document.
4.2. The importance of extension and research collaboration
For decades, we know that the possibilities for expansion of cropland declined steadily over the entire planet. Due to deforestation and erosion of slopes, this approach increases the risk of environmental degradation in many parts of the world. That is why the only viable options that remain are increasing yields and the change in product mix. Increasing agricultural productivity is the more urgent task than any other task, the majority of the poor in the developing world live in rural areas, and the productivity of the sector is actually in decline in many low-income countries. The use of irrigation can dramatically improve yields. The adoption of irrigation requires training of farmers and provides extension services for a long time, but it can lead to a substantial increase in yields without intervention by agricultural research. Yet in most parts of the world, the possibilities of expansion of irrigated areas are also limited and, indeed, many of those already suffering from salinization, water-logging and other issues affecting productivity. Therefore, if it is done unsuitable application to better manage irrigation and extending the yield perimeters, it is impossible to rely on it for the physical basis of agricultural production increases which the world will much need in the future. Meeting this challenge comes mainly heavy agricultural technology development and transfer systems improved, i.e., research and agricultural extension. It also depends on the education systems of farm families; some observers even argue that education is the most important factor in improving productivity.
Fulginiti and Perrin [11] have reviewed the literature on the evolution of agricultural productivity in several countries and made their own estimates using other methodological approaches. They observed that all developed countries have experienced their agricultural productivity increase, whereas most low-income countries have seen their productivity decline, even when they have widely adopted the varieties of wheat and rice from the green revolution. Based on their own analysis, they concluded that the productivity decline is real and that the adverse agricultural pricing policies can be a major cause. The overall picture is however mixed. Masters et al. [12] found improvements in cereal yields in some districts of 13 African countries recently.
Despite some positive points, we can say that the performance of agricultural productivity in developing countries have been encouraging. It is clear that agricultural technology systems of these countries face a major challenge, which is likely to further intensify in the future and which is exacerbated by the general trend of reduced funding for agricultural research observed in developing countries over the past decades.
4.3. Research and extension policy
Research and extension systems play a crucial role in all sectors, especially agricultural and rural development [13]. As is known all over the world, one of the main purposes of extension is to increase the level of living standards of the rural family through nonformal education. Extension has a very crucial role to play in sustainable development [14]. Moreover, they are central to realizing the potential of agricultural innovation. Many developing countries, however, do not have sufficient resources to properly develop their capacity for innovation. More specifically, the activities of agricultural research institutions are often affected by scarce investments and poor financial management, as well as limitations in technology transfer strategies [13]. For that reason, to develop and enhance research and extension relations by suitable policy acts. In this context, it is necessary to make a strong effort to ensure that the findings of the research reach the producer as well as to the adoption. It is absolutely necessary to bring research and extension together.
Modern technologies and innovation are very important for rural development of many developing countries; these technologies are developed by research institutes or universities or in the other case imported from developed countries, which are leading in field of research and development. Two factors seemed to be very important for farm operators’ technology use on farms: a public and private organization engaged in dissemination of recent innovations and technologies to rural areas and second is socioeconomic characteristics and information seeking behaviors of farm operators, which influence their decision to select information sources of information. It is necessary for farm operators to know about how to carry various framing practices like soil preparation, selection of seed and sowing techniques, fertilizations, disease and pest management, irrigation and harvesting, and storage, for the reason of survival in competitive market and more stable income [15]. Agricultural information systems have very important role in rural development. Besides new information create; professional expert and suitable input via several factors is required to develop. Factors those take place in the list are rural folks, field staff of extension organizations, nongovernmental organizations (NGOs), and research institutes, becoming increasingly popular in many countries local municipal authorities [16]. Despite the fact that rural population requires extension services, training, and access to information, share of budget endowed is very limited around the globe [17].
It is necessary to clarify the topic here. In particular, R&D refers to the process leading up to the introduction of a technology. However, even if it is not prevailing in sector like industry where professional sense is dominant, but there are many difficulties in quickly and timely reaching findings of research to target groups, especially in the agriculture sector of developing countries. It is necessary that the technologies that are produced are derived from the old technology and are used rapidly in the production process. From this perspective, research and extension (R&E) is equally important like research and development (R&D). Because there is risk that developed technologies will remain in libraries or electronic data banks without coming in force. From this perspective, budget, time, and human resources are used at great cost to produce these research findings or output, their effectiveness and dissemination is important. For this reason, R&E is important like R&D [18]. There are many ancient examples, especially of not taking advantage of technologies produced in rural areas. Maximum efforts should be made to ensure that research and extension get equal importance.
5. Conclusion
Human capital is the most important strategic factor for development and also rural development; as new technologies emerge, the market demand for better and healthier products and consumer demand in terms of quality and delivery time are changing. In today’s world, it becomes increasingly important to know how information can be accessed, how it is adopted, and how it can be assimilated. In this respect, each country allocates budget for training, education, and extension according to its own conditions. This budget may be intended for rural community-based social assistance, but the economic and welfare effect is essential. In this way, it is aimed to increase the living standards of the families living in the rural areas. This will naturally contribute to national income and to the prosperity of society.
When Turkey is taken as an example to have huge experiences. in 1846, Agricultural education was started in Turkey. However, the main developments on agricultural research and extension have occurred since the 1930s [19]. Generally, it is developing policies to increase public prosperity in the new millennium in the framework of these developments. According to this, since the beginning of 2000, Turkey with its policies has made important leaps in the field of human, science, and technology development. Turkey has made significant even radical changes in science and R&D policies. Between 2003 and 2004, while increasing its National Income from 305 to 800 billion dollar and increasing GDP 2.6 times, Turkey increased it R&D expenditures seven times. Therefore, the share of R&D has increased to above 1% of GDP in 2015, which was 0.48% in 2003. Private sector stands first with 49.8% in R&D expenditures, followed by higher education (40.5%) and public institutions (9.7%). In 2014, 51% of R&D expenditures are financed by commercial sector, 26% by public, 18% by higher education, 3% by other domestic resources, and 1% by foreign sources. Agriculture share in R&D expenditure are 4%, out of 572 million dollar 51% is by public sector, 46% by higher education, and 3% by private sector [20]. As a result, in order to reach 2023 targets, Turkey is determined to carry out its consistent and innovative policies in the science and technology field. With increasing R&D share from 1 to 3% and R&E integrity, it will achieve significant improvements in livestock and crop production sector like other in other sectors.
As a result, all over the world, particularly in developing countries, that R&E is very important and should be considered at least as much as R&D. However, it will be ensured that societies meet with the technology produced. For this, the development of human resources should be emphasized and a suitable atmosphere should be prepared for this widespread prosperity.
\n',keywords:"human development, technology, agriculture, research and extension, research and development",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/56708.pdf",chapterXML:"https://mts.intechopen.com/source/xml/56708.xml",downloadPdfUrl:"/chapter/pdf-download/56708",previewPdfUrl:"/chapter/pdf-preview/56708",totalDownloads:1105,totalViews:1007,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:1,dateSubmitted:"November 9th 2016",dateReviewed:"April 10th 2017",datePrePublished:null,datePublished:"September 6th 2017",dateFinished:"August 10th 2017",readingETA:"0",abstract:"Human capital is the most important strategic factor for development; as new technologies emerge, the market demand for better and healthier products and consumer demand in terms of quality and delivery time are changing. In today’s world, it becomes increasingly important to know how information can be accessed, how it is adopted, and how it can be assimilated. In this respect, each country allocates budget for training, education, and extension according to its own conditions. This budget may be intended for rural community-based social assistance, but the economic and welfare effect is essential. In this way, it is aimed to increase the living standards of the families living in the rural areas. This will naturally contribute to national income and to the prosperity of society. The subject has been discussed generally in the world, especially in the case of Turkey. According to this, all over the world, particularly in developing countries, research and extension (R&E) is very important and should be considered at least as much as research and development (R&D). However, it will be ensured that societies meet with the technology produced. For this, the development of human resources should be emphasized and a suitable atmosphere should be prepared for this widespread prosperity.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/56708",risUrl:"/chapter/ris/56708",book:{slug:"research-and-development-evolving-trends-and-practices-towards-human-institutional-and-economic-sectors-growth"},signatures:"Orhan Özçatalbaş",authors:[{id:"170206",title:"Prof.",name:"Dr. Orhan",middleName:null,surname:"Özçatalbaş",fullName:"Dr. Orhan Özçatalbaş",slug:"dr.-orhan-ozcatalbas",email:"oozcatalbas@gmail.com",position:null,institution:{name:"Akdeniz University",institutionURL:null,country:{name:"Turkey"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Education and extension for development",level:"1"},{id:"sec_3",title:"3. Human development and research and development (R&D)",level:"1"},{id:"sec_4",title:"4. A research and development (R&D) equipped with extension",level:"1"},{id:"sec_4_2",title:"4.1. Research and development policy",level:"2"},{id:"sec_5_2",title:"4.2. The importance of extension and research collaboration",level:"2"},{id:"sec_6_2",title:"4.3. Research and extension policy",level:"2"},{id:"sec_8",title:"5. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'UNDP. Transforming Our World: The 2030 Agenda For Sustainable Development, Sustainable Development Knowledge Platform [Internet]. New York, NY, USA; 2017. Available from: https://sustainabledevelopment.un.org/post2015/transformingourworld'},{id:"B2",body:'Öztürk H. Eğitim Sosyolojisi, Hatipoğlu yayınları: 55, Yükseköğrenim dizisi: 13. Ankara; 1993. p. 206. ISBN: 975-7527-27.0r'},{id:"B3",body:'Psacharopoulos G, Woodhall M. 1985. Education for Development. The World Bank. (Eğitimin Ekonomik Büyümeye Katkısı), Eğitim Ekonomisi Seçilmiş Yazılar. Translate vy Y. Kavak ve B. Burgaz. Pegem yayın no: 14 Ankara. 1994. pp. 39-46. ISBN: 975-7251-06-2'},{id:"B4",body:'UNDP. Human Development Report 2015: Work for Human Development. 1, UN Plaza, New York, NY, USA: The United Nations Development Programme; 2015'},{id:"B5",body:'UNDP. United Nations Human Development Report 2009 [Internet]. 2010. Available from: http://en.wikipedia.org/wiki/File:UN_Human_Development_Report_2010_1.PNG'},{id:"B6",body:'UNDP. International Human Development Indicators [Internet]. NY, USA: The United Nations Development Programme; 2000-2010. Available from: http://hdr.und.org/en/statistics/'},{id:"B7",body:'OECD. Economic Situation, Analysis and Projections [Internet]. 2010. Available from: http://www.oecd.org/topic/0,3699,en_2649_34573_1_1_1_1_37443,00.html'},{id:"B8",body:'Grigg DB. Agricultural Systems of the World: An Evolutionary Approach. Cambridge, UK: Cambridge University Press; 1974'},{id:"B9",body:'OECD. Proposed Standard Practice for Surveys on Research and Experimental Development. Paris Cedex 16, France: Frascati Manual; 2002. ISBN 92-64-19903-9'},{id:"B10",body:'TÜBİTAK. Vizyon 2023. Ankara: The Scientific and Technological Research Council of Turkey; 2016. Available from https://www.tubitak.gov.tr/tr/kurumsal/politikalar/icerik-vizyon-2023'},{id:"B11",body:'Fulginiti LE, Perrin RK. Agricultural productivity in developing countries. Agricultural Economics. 1998;19:45-51'},{id:"B12",body:'Masters WA, Bedingar T, Oehmke J. The impact of agricultural research in Africa: Aggregate and case study evidence. Agricultural Economics. 1998;19(1-2):81-86'},{id:"B13",body:'FAO. Research and Extension. Rome: The Food and Agriculture Organization of the United Nations; 2016. Available from http://www.fao.org/research-and-extension/en/'},{id:"B14",body:'Özçatalbaş O, Brumfield R. Allelopathy as an agricultural innovation and improving allelopathy extension. Journal of Food, Agriculture and Environment. 2010;8(2):908-913'},{id:"B15",body:'Boz İ, Özçatalbaş O. Determining information sources used by crop producers: A case study of Gaziantep province in Turkey. African Journal of Agricultural Research. 2010;5(10):980-987'},{id:"B16",body:'Ramirez R. Communication: A Meeting Ground for Sustainable Development. Ontario, Canada: School of Rural Extension Studies University of Guelph; 1999. Available from: http://www.fao.org/waicent/faoinfo/sustdev/CDdirect/CDre0037.htm'},{id:"B17",body:'Snapp SS. Innovations in extension from Malawi. American Society for Horticultural Science, HortTechnology. 2004;14(1)'},{id:"B18",body:'Özçatalbaş O. Tarımda Teknoloji Kullanımı ve AR-YA (The Use of Technology in Agriculture and Research and Extension). Gıda Tarım ve Hayvancılık Bakanlığı, Tarım Ekonomisi Proje Toplantısı, 13 Şubat 2017, konferans, Belek Antalya. 2017'},{id:"B19",body:'Senocak C. Extension and Communication. Ankara: Guzel Sanatlar Publ.; 1967. p. 439'},{id:"B20",body:'Turkstat. Araştırma Geliştirme Faaliyetleri Araştırması [Internet]. Türkiye İstatistik Kurumu (Turkish Statistical Institute); 2015. Available from: www.tuik.gov.tr [Accessed: November 17, 2015]'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Orhan Özçatalbaş",address:"oozctalbas@gmail.com",affiliation:'
Department of Agricultural Economics, Akdeniz University, Antalya, Turkey
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1. Introduction
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Most of the Brazilian thermoelectric park is completely shut down for months whenever the hydrological situation is favorable. As in the recent historical average hydroelectric generation has been 90% of its generation capacity for the system [1], idleness has prevailed in the thermal park since the plants can only be activated when the hydroelectric reservoirs are below 50% of its maximum volume. The contrast with the international reality is striking. In most countries, power plants with combined cycle of coal or gas typically do not experience inactivity during a long time period. Instead, they operate at the base of the system, being dispatched almost continuously. Additionally, thermals that in other electrical systems are used for generation of tip, with daily activation or at least in good part of the working days, such as open or thermal cycle gas engines with motors, in Brazil, can remain idle for long because they are not necessary in normal or favorable hydrology situations.
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On the other hand, it is necessary to ensure the supply of electricity to consumers within standards of continuity and reliability. Besides, the lack of investments in the industry causes the loss of product quality, and the excess of investments can make the value of the product very expensive, disheartening its wear [2, 3].
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To guarantee the quality and reliability of the electric power supply, it is necessary to perform an optimal load dispatch [2, 4]. Too many papers presented in the literature develop the load dispatch of the thermal plants considering that all the engines of the plant have good technical conditions, but this is not always true, so in this chapter, a method is presented for the pre-dispatch of load that takes into account the technical state of the plant’s motors through diagnosis and making use of fuzzy logic.
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The development of a computational tool to support the decision of cargo dispatch according to the operating conditions of the engines and generators of thermal plants is proposed, which are classified in relation to the probabilities of failure by a fuzzy system developed in this thesis, from indicators obtained from lubricant oil analysis, vibration analysis, and thermography of power generation equipment. The basis for the study is based on the principle of operation and operational conditions of the equipment to be dispatched for generation in a thermal plant, besides its particularities as specific consumption and the quality of pollutant sent by each equipment.
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2. Maintenance systems and their application in thermoelectric plants
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The ability of a generation source to meet an energy demand can be influenced by unexpected units of power-generating units. The tests were even more advanced to repair preventive maintenance measures but were not revised in the 1990s with maintenance and maintenance work on engines and generators.
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In recent times, condition-based maintenance (CBM) has been introduced in industrial systems to preventively maintain the right equipment at the right time relative to its current “operating condition.” The good state of operation of a generator can be represented mainly by conventional indicators such as oil temperature, harmonic data, vibration, etc. Then the importance of monitoring the motors/generators and their diagnosis for the dispatch of cargo to not have unexpected interruptions.
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Most energy generation unit scheduling packages are considered preventive maintenance schedules for units over an operational planning period of 1 or 2 years in order to defray the total operation while meeting the requirements of system power and maintenance restrictions. This problem consists of verifying the generating units must be stopped from production. The generating unit should be regularly examined for safety. It is important to detect a failure in a power generation unit that can be used in the machines. The main indication is a suitcase case response. Therefore, the fixation and the key point are used in the proposed methodology. The issue is addressed as an optimization problem. The model is developed by determining the objective function, which is a net power reserve of the unit [5].
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They point out that condition-based maintenance (CBM) is an approach that gathers and assesses information in real time, and based on this information, it recommends maintenance decisions based on the existing condition of the system. In the last decades, research on CBM has been rising rapidly due to the increment of computer-enabled monitoring technologies. It has been proved that CBM, when it is planned carefully, can improve the reliability of equipment reducing costs [6].
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The factor of diagnostic importance (DIF) is frequently used for choosing preferences in maintenance activities at power supply sections of distribution systems. In [7] approach to assess a weighted cumulative diagnostic importance factor (WCDIF) for each section, which represents a good parameter for the ordering of maintenance activities, is developed. The methodology includes the effects of distributed generation (DG) and the loads. It was implemented as case studies in two distribution systems, so that, in the end, sorting lists of feed sections for maintenance activities were obtained [7].
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In order to improve the reliability and efficiency of equipment, it is very important to apply the condition-based maintenance (CBM). A good maintenance activity has a close relationship with security and diminishes costs, making this issue even more attractive to researchers [8].
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Proper maintenance can increase the company’s productivity and increase its value in the market. The main study provided a robust model that can evaluate strategically important available technology and may exclude outdated and/or inappropriate technology. There are many researches in this field in which the number of models has been proposed, such as the maintenance management system, maintenance performance measurement, and maintenance performance indicators, but the details of the effectiveness of the predictive maintenance indicator specifically based on maintenance and conditions (MBC) with maintenance and management requirements using the analytical hierarchy (AHP) process are hardly available in the literature [9].
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Basically, the process consists of monitoring parameters that characterize the state of operation of the equipment. The methods employed involve techniques and procedures for measuring, monitoring, and analyzing these parameters [10]. It can be related as oil analysis, ferrography, thermography, and vibration analysis.
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Motor operation data in conjunction with vibration, oil, and temperature analysis data are collected periodically at the plant and are used in an integrated way to feed a fuzzy rule-based system, which returns the pre-dispatch scheduling of the plant for the period of interest, taking into account the state of operation of the machines (Figure 1).
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Figure 1.
Methodology. Source: Authors.
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Thermal power plants involve many mechanical and electrical systems that require constant analyzing of power production. The data obtained through this analysis are necessary for a good operation, maintenance, and evaluation of the performance of the plants. For this analysis the so-called distributed control systems (DCS) are often used. Nevertheless, the obsolescence of this equipment increases the risks of unavailability of the generating units, mainly in thermoelectric plants, where mechanical wear is elevated, due to the high temperatures and the chemical agents used for the production of electric energy [11].
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Mean time between failures (MTBF) or mean period between failures is a value assigned to a particular equipment to describe its reliability. This value indicates when a device failure may occur. When this index is high, the reliability of the equipment and, consequently, the maintenance will be also evaluated as excellent.
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The average (MTTR) time for repair is a measure on the basis of repairable item maintenance. It represents the average time required to repair a component failure or mathematically expressed equipment, that is, corrective maintenance.
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Oil analysis: The initial purpose of oil analysis of a lubricated assembly or a hydraulic system is to economize by optimizing the intervals between the exchanges. As the analyses carried out resulted in indicators that report on the wear of the lubricated components, the second objective of this process became defect control for predictive maintenance [12].
In the upper left, you can see the simplification of the eight pillars of the TPM, for four pillars.
The left-center part shows the diagnostic activities that allow to know the technical state of the motors, to know whether or not they can be used in the pre-dispatch of load.
In the lower left, we show the reliability analyses, which together with the diagnosis allow us to know when it is possible for each motor to fail to consider it in the pre-dispatch of load.
The right part shows the application of the fuzzy logic, to perform the pre-dispatch of load, according to the fuzzy rules that meet the technical state of the engines.
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3. Fragments used for implant and end methodology TPM program
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This chapter presents a new solution proposal, which includes the pre-dispatch of load focused on the operational conditions of the machines using computational tools, specifically fuzzy logic. This application incorporates some novelties, such as good maintenance management through TPM program for decision-making, including performance indicators of the generating units such as vibration, lubricating oil, and temperature, analyzing if the generating unit will operate and maintain reliability or will get into maintenance due to poorly diagnosed performance.
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3.1 Pillar of specific improvements (recommended group: coordinators of ME, MA (plant managers), MP, and SMA)
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Objective: To maximize the overall efficiency of the equipment and the operation through the analysis and elimination of operational losses (Table 1).
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Pillar of specific improvements
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Evaluation/progress/criteria
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Evaluation/progress/criteria
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Background/objective
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Expected condition
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1. Elaborate complete and detailed flow of the operation, identifying the various auxiliary engines and equipment, their respective priorities, and main risks. Note the current conditions so that you can compare after the improvements are implemented
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To increase the knowledge of the whole operation and to standardize the knowledge of the participants of the working group, using the tools of quality
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Working group formed, operational flow completed in a clear and didactic way, equipment, priorities, and main risks identified and being known by all participants
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\n
\n
2. Identify the generation capacities in MW of each engine/plant—standard and real—and the current losses of the operation, quantifying through the Pareto chart
\n
Identify the distortions between the actual and expected (standard or standard) of each engine/plant. Identify fuel and lubricant/engine/plant consumptions, knowing the performance of each one to be able to act on improvements
\n
Motors, auxiliary equipment, and operations identified with their nominal and actual capacities Criterion to analyze and identify the main losses of the operation, stratify, and classify graphically in A, B, and C (Pareto)
\n
\n
\n
3. Investigate losses in detail according to the priority grades I, II, and III of the chart, presenting alternatives for reducing or eliminating current losses found for later comparison
\n
Allow to identify the fundamental causes of each selected loss, the actual operating conditions of each motor/auxiliary equipment (clearances, paint, leaks, instrumentation, working environment conditions, qualification of operators, necessary and available tools, etc.)
\n
Use of the MASP tools to analyze and solve identified losses. PDCA, Fishbone, 5W2H Methodology being used to investigate and eliminate losses
\n
\n
\n
4. Prepare detailed action plan for the chosen losses, and develop a schedule of activities, following the MASP methodology
\n
Organize the various activities necessary to eliminate identified losses, in order of priority (from highest to lowest) and investment (from lowest to highest)
\n
Plan of action prepared by the working group with actions, responsibility, deadlines, and progress of the activities chosen in the item above, through the MASP tools Put the action plan into practice, and compare the results before and after
\n
\n
\n
5. Standardize operational procedures, ensuring that engines and auxiliary equipment are operated within the required conditions of pressure, temperature, speed, rpm, etc.
\n
After achieving the expected results, standardize the procedures that should be followed by all operators
\n
Interim operational standard completed and being used by the operators in each engine and auxiliary equipment
\n
\n
\n
6. Analyze the existing operational reports, and make the necessary modifications to improve the quality of the annotated information, including maintenance stops by motor or auxiliary equipment, lack of spare parts, labor problems, transportation, etc.
\n
Improving the quality of information to assist in the investigation of losses and their eliminations
\n
Performance of the operation/motor and auxiliary equipment being evaluated by comparing the indicators and objectives defined for each engine/plant. Information of the operational reports being provided with quality and accompanied by the managers, supervisors, and operators. No data distortion
\n
\n\n
Table 1.
Pillar of specific improvements.
Source: Authors.
\n
\n
\n
3.2 Automatic maintenance pillar (recommended group: managers, supervisors, and operators of each plant)
\n
Objective: To enable the operators to keep their workplaces clean and organized, inspecting their equipment, following operating procedures, lubricating and identifying abnormalities, and labeling and attempting to eliminate hard-to-reach places and sources of dirt (Table 2).
\n
\n
\n
\n
\n\n
\n
Automatic maintenance pillar
\n
Valuation/progress/criteria
\n
\n
\n
Evaluation/progress/criteria
\n
Background/objective
\n
Expected condition
\n
\n\n\n
\n
1. Determine the procedure and how to identify abnormalities through labels. Determine labeling procedures, label types, and colors
\n
Eliminate abnormalities of motors, auxiliary equipment, installations, workplace, and accumulated dirt, eliminate unused materials in the operation, visually identify the abnormal conditions that need to be repaired, and maintain the ideal working conditions that meet Industrial Safety. In this initial cleaning, the conditions of motors, auxiliary equipment, and installations such as loose bolts, lack of fixings and protections, damaged parts and temporary repairs, lack of signaling, etc., identified each with a label, and providing the necessary repairs must be observed. The label must only be removed after approval of the service performed
\n
At initial cleaning, operators and personnel involved must be trained to identify abnormalities in motors, auxiliary equipment, facilities, and workplaces through stickers Areas, engines, auxiliary equipment, and facilities must be clean and maintained in this condition, no longer tolerating any signs of clutter and dirty locations. Use the 5S Locations that are not meeting this requirement should at least be flagged and their future repair be included in a timely, responsible action plan
\n
\n
\n
2. Train participants to identify abnormalities of motors, auxiliary equipment and work area through labeling
\n
\n
\n
3. Perform initial cleaning on all motors, auxiliary equipment, and operating areas, determining ideal working conditions (no leakage, good flooring, motors, auxiliary equipment and facilities, painted and corrosion-free, with necessary signaling, conditions security, etc.)
\n
\n
\n
4. Prepare planning/schedule to carry out the necessary activities of removal of the labels placed in places that presented abnormalities
\n
Monitor the activities performed, and measure the results after the improvements implemented
\n
After label placement, a control should be created indicating the type of problem, the number of labels placed and removed, and the areas involved in the abnormalities, such as maintenance, operation, safety, and environment. Identification, simple, and objective control
\n
\n
\n
5. Establish the basic conditions of engines, auxiliary equipment and facilities, workplaces, floors, walls, lighting, painting, signaling, temperatures, etc.
\n
Ensure operation within the ideal standards required
\n
Ideal conditions for motors, auxiliary equipment, signed installations, and work areas, with industrial safety colors, nameplates, lighting, and cleaning
\n
\n\n
Table 2.
Automatic maintenance pillar.
Source: Authors
\n
\n
\n
3.3 Planned maintenance pillar (recommended group: PM coordinator, service managers and supervisors, and each plant)
\n
Objective: To create a maintenance management corporate model for all engines and auxiliary equipment of the plants and external clients and to optimize interventions and reduce maintenance costs, ensuring the performance of auxiliary engines and equipment (Table 3).
\n
\n
\n
\n
\n\n
\n
Planned maintenance pillar
\n
Evaluation/progress
\n
\n
\n
Evaluation/progress/criteria
\n
Background/objective
\n
Expected condition
\n
\n\n\n
\n
1. Elaborate and approve methodology to prioritize engines, auxiliary equipment, and facilities in A, B, and C and disclose to all OPM coordinators. Determine form of identification and approve with the steering committee
\n
Standardize how to prioritize engines, auxiliary equipment, and facilities as the company needs, with a focus on business
\n
Complete prioritization worksheet containing pertinent questions from the areas involved in the operation (operation, maintenance, engineering, safety, and environment)
\n
\n
\n
2. Determine how and when the meeting involving operation, engineering, maintenance, safety, and environment will be made to define all the engines, auxiliary equipment, and facilities of each plant in A, B, or C
\n
Identify the company’s business priorities to facilitate the deployment of a maintenance management model
\n
Meeting to evaluate and classify in A, B, and C all engines, auxiliary equipment, and facilities of the company, marked or performed with the areas involved
\n
\n
\n
3. After completion, visually list and identify priorities A, B, or C to facilitate supervision
\n
Facilitate service and decision in the most appropriate action to be taken, according to priority
\n
All motors, auxiliary equipment, and facilities, classified in A, B, and C with the visual identification labels, according to the model approved and adopted by the company
\n
\n
\n
4. Identify the current state of each engine, auxiliary equipment, and installation, inspect/review, and make necessary repairs to maintain in perfect operational conditions
\n
Rescue the ideal operating conditions of engines, auxiliary equipment, and facilities, improving availability, reliability, and maintenance
\n
Inspection/revision planning in engines, equipment, and facilities A to redeem desired conditions Action plan defined with activities, materials, deadlines, time provided in each repair activity, and maintenance team
\n
\n
\n
5. Elaborate the most indicated maintenance procedures for each engine, auxiliary equipment, and facilities, as recommended in the master plan Consider those in the technical manuals, MaMa2i, and create those that do not exist and are necessary
\n
Define a maintenance philosophy to be used in equipment A, B, and C according to priority
\n
Equipment A, B, and C classified and with the type and recommended maintenance plan completed
\n
\n
\n
6. Start the required activities for each engine, auxiliary equipment, and installation A. Follow the MaMa2i plan, and add non-existing services to the system
\n
Organize maintenance, and update the data of each engine, auxiliary equipment, and installation A, creating history, technical inspection standards, and maintenance procedures Follow template created by engineers for reference
\n
Planning and schedule of activities for engines, auxiliary equipment, and facilities A, completed and started
\n
\n\n
Table 3.
Planned maintenance pillar.
Source: Authors
\n
\n
\n
3.4 Pillar of education and training (recommended group: this pillar is corporate and only depends on HR)
\n
Objective: To support the other pillars, analyzing the qualification of participants and the need for training. Responsible for communication, TPM disclosure, event planning, and compliance with the basic program guidelines to facilitate documentation, reduction of dissemination costs, and support material (Table 4).
\n
\n
\n
\n
\n\n
\n
Pillar of maintenance education and training
\n
Evaluation/progress
\n
\n
\n
Evaluation/progress/criteria
\n
Background/objective
\n
Expected condition
\n
\n\n\n
\n
1. List all employees who have already been trained and those who require basic OPM training to participate in the work groups
\n
Level the knowledge of all participants before starting to develop the activities in the working groups
\n
All employees participating in the OPM program identified to receive the basic training provided by the pillar coordinators
\n
\n
\n
2. Elaborate and make available in the network a basic training to minister to all the employees and in the integration of new ones
\n
Standardize the material and information passed to employees
\n
Teaching material for the basic training, completed, approved by the steering committee, and made available to the pillar coordinators
\n
\n
\n
3. Determine the dates of the training of each pillar and the person in charge of ministering
\n
Organize a schedule of activities to monitor, and audit the development of the TPM
\n
Planning/schedule of training to be performed, indicating employees, dates, and instructors TPM training for integration of new employees, completed to be incorporated by HR
\n
\n
\n
4. After the training, disseminate the number of participants to serve as an evaluation indicator of the pillar in the TPM program
\n
To measure the degree of PMS development and to present the ET pillar indicators
\n
Constant and updated dissemination of the number of employees trained and hours of training performed
\n
\n
\n
5. Make competency map of all participants in the working groups, to identify the qualification, knowledge, and needs
\n
Identify the need for training, planning, and implementation in order to allow the activities of the other OPM pillars to proceed
\n
Worksheet of skills and qualification of the maintainers and operators completed, indicating the basic and specific training required
\n
\n
\n
6. Elaborate internal/external training plan, one-point training, MASP, and lectures to adapt the knowledge need of each work group participant
\n
Level the knowledge of the working group participants so they can take on other activities without any problem, according to the steps of the ME, MA, MP, and SMA pillars
\n
Planning to carry out the training identified in the previous item, including lectures, one-point training, MASP, etc.
\n
\n\n
Table 4.
Pillar of maintenance education and training.
Source: Authors
\n
Members of the audit pillar checklist should meet monthly to discuss the MTBF goals and the monthly MTTR and other activities corresponding to the maintenance management program (Table 5).
\n
\n
\n
\n
\n\n
\n
Activity name
\n
Responsibility
\n
Check list
\n
\n\n\n
\n
Check cleaning of areas
\n
Plant manager
\n
Check leaks, state of conservation, paint, and signage
\n
\n
\n
Check the binder where the “cleaning pattern” of the area is located
\n
Supervisors
\n
Check if the cleaning pattern plug is placed in an easy to read location
\n
\n
\n
Check if the tags are in control
\n
Plant manager
\n
Check in the label control software if there is any movement of placement of new labels
\n
\n
\n
Check action plan to remove labels
\n
Plant manager and supervisors
\n
Check in the label control software if there is an action plan for removing the labels
\n
\n
\n
Check signage of plants
\n
Plant manager and supervisors
\n
Check standardization of signaling
\n
\n
\n
Check GPM frame of the plant
\n
Engineering
\n
Check if the information is up to date
\n
\n\n
Table 5.
Check list pillar auditoria.
Source: Authors.
\n
\n
\n
\n
4. Predictive maintenance using computational (fuzzy logic) decision support tool in preload dispatch
\n
An application of fuzzy logic is justified by the ability to anticipate the possibilities of making the pre-dispatch time of the load on the operational tasks of the equipment.
\n
This study deals with the application of fuzzy logic to load dispatch, but with a particularity that is to perform the said pre-dispatch of load taking into account the technical state of the engines, evaluated by different variables related to maintenance. In the first part, the development of the fuzzy rules and of the whole procedure of inference is exposed, and in the second part, all the tests evaluate the maintenance and the technical state of the motors. This tool served as the basis for the resolution of the real problem of pre-dispatch of cargo to satisfy the rationalized methods of just in time of the thermal plant on the operational conditions of the equipment (Figure 2).
\n
Figure 2.
System based on fuzzy logic. Source: Authors.
\n
A system based on fuzzy logic, as shown in Figure 2, can have its action schematized by the following constituent elements: fuzzifier; rules, or knowledge base; inference, or logical decision-making, and Defuzzifier [13].
\n
In the first part the development of the fuzzy rules and of the whole procedure of inference is exposed and in the second part all the tests to evaluate the maintenance and the technical state of the motors. This tool served as the basis for the resolution of the real problem of pre-shipment of cargo to satisfy the rationalized methods of just in time of the thermal plant on the operational conditions of the equipment [14, 15].
\n
The fuzzy system models the style of reasoning, imitating the capability to make decisions in an environment of uncertainty and imprecision. In this way, fuzzy logic is an intelligent technology, which provides a mechanism to manipulate imprecise information—concepts of small, high, good, very hot, cold—and that is able to infer an estimated answer to a question based on an inexact, incomplete knowledge, or not fully reliable information.
\n
The development of a computational tool supports the load dispatch according to the location of motors and generators for thermal energy, analyzing the main thermoelectric generation variables for the entire predictive maintenance process.
\n
All variables are inserted considering the intervals determined in the rules of inference as shown below.
\n
The computational interface was useful for the search of some preselected characteristics to enable its implementation. Tables 6–12 show such characteristics and respective purposes.
\n
\n
\n
\n
\n
\n
\n\n
\n
Class
\n
1-[N] Normal
\n
2-[P] Permissible
\n
3-[A] Alert
\n
4-[C] Critical mm/s
\n
\n\n\n
\n
(Class I)
\n
(0.18–0.71)
\n
(0.71–1.80)
\n
(1.80–4.50)
\n
(Above 4.50)
\n
\n
\n
(Class II)
\n
(0.18–1.10)
\n
(1.10–2.80)
\n
(2.80–7.10)
\n
(Above 7.10)
\n
\n
\n
(Class III)
\n
(0.18–1.80)
\n
(1.80–4.50)
\n
(4.50–11.2)
\n
(Above 11.2)
\n
\n
\n
(Class IV)
\n
(0.18–2.80)
\n
(2.80–7.10)
\n
(7.10–18.0)
\n
(Above 18.0)
\n
\n
\n
\n
A
\n
B
\n
C
\n
D
\n
\n\n
Table 6.
Manufacturer vibration levels.
Source: Authors
\n
\n
\n
\n
\n\n
\n
Zone
\n
Qualification
\n
Operation of machines
\n
\n\n\n
\n
Zone A
\n
[N] Normal 0.18–2.80 mm/s
\n
Commissioned machines should generally operate in this area
\n
\n
\n
Zone B
\n
[P] Permissible 2.80–7.10 mm/s
\n
It is acceptable for unrestricted operation for long periods
\n
\n
\n
Zone C
\n
[A] Alert 7.10–18.0 mm/s
\n
Unsatisfactory for continuous operations for long periods
\n
\n
\n
Zone D
\n
[C] Critical above 18.0 mm/s
\n
It is sufficient to cause damage to the machine at any time
\n
\n\n
Table 7.
Vibration severity rating relevance function.
Source: Authors.
\n
\n
\n
\n
\n
\n\n
\n
Class
\n
1-[N] Normal
\n
2-[A] Alert
\n
3-[C] Critical
\n
\n\n\n
\n
(Water% volume)
\n
(% ≤ 0.2)
\n
(0.3)
\n
(Above 03)
\n
\n
\n
(Micron iron content)
\n
(% ≤ 49)
\n
(50)
\n
(Above 51)
\n
\n
\n
(Micron copper content)
\n
(% ≤ 1)
\n
(20)
\n
(Above 21)
\n
\n
\n
\n
A
\n
B
\n
C
\n
\n\n
Table 8.
Lubricating oil.
Source: Authors.
\n
\n
\n
\n
\n\n
\n
Zone
\n
Qualification
\n
Operation of machines
\n
\n\n\n
\n
A
\n
[N] Normal
\n
Commissioned machines should generally operate in this area
\n
\n
\n
Water% volume
\n
% ≤ 0.2
\n
\n
\n
Micron iron content
\n
% ≤ 49
\n
\n
\n
Micron copper content
\n
% ≤ 19
\n
\n
\n
B
\n
[A] Alert
\n
Unsatisfactory for continuous operations for long periods
\n
\n
\n
Water% volume
\n
0.3
\n
\n
\n
Micron iron content
\n
50
\n
\n
\n
Micron copper content
\n
20
\n
\n
\n
C
\n
[C] Critical
\n
It is sufficient to cause damage to the machine at any time
\n
\n
\n
Water% volume
\n
Above 0.3
\n
\n
\n
Micron iron content
\n
Above 51
\n
\n
\n
Micron copper content
\n
Above 21
\n
\n\n
Table 9.
Function of pertinence of the severity according to the oil.
Source: Authors.
\n
\n
\n
\n
\n\n
\n
(Zone)
\n
(Thermography)
\n
\n\n\n
\n
(A)
\n
([N] Normal less or equal 94.0 F)
\n
\n
\n
B
\n
[P] Permissible
\n
\n
\n
(B/C)
\n
(94.0 F)
\n
(164.2 F)
\n
\n
\n
C
\n
[A] Alert
\n
\n
\n
(C/D)
\n
(164.2 F)
\n
(199.3 F)
\n
\n
\n
(D)
\n
[C] Critical above 199.3 F
\n
\n\n
Table 10.
Thermography to determine hot spots.
Source: Authors
\n
\n
\n
\n
\n\n
\n
Zone
\n
Qualification
\n
Operation of machines
\n
\n\n\n
\n
Zone A
\n
[N] Normal (T ≤ 34.5°F)
\n
Commissioned machines should generally operate in this area
\n
\n
\n
Zone B
\n
[P] Permissible (34.5°F < T ≤ 73.5°F)
\n
It is acceptable for unrestricted operation for long periods
\n
\n
\n
Zone C
\n
[A] Alert (73.5°F < T ≤ 93°F)
\n
Unsatisfactory for continuous operations for long periods
\n
\n
\n
Zone D
\n
[C] Critical (T > 93°F)
\n
It is sufficient to cause damage to the machine at any time
\n
\n\n
Table 11.
Function of pertinence of the classification of thermography.
Source: Authors
\n
\n
\n
\n
\n\n
\n
Motor technical status (ETM) for operating conditions
\n
Operation of machines
\n
\n\n\n
\n
Normal
\n
76–100%
\n
Commissioned machines should generally operate in this area
\n
\n
\n
Permissible
\n
51–75%
\n
It is acceptable for unrestricted operation for long periods
\n
\n
\n
Alert
\n
26–50%
\n
Unsatisfactory for continuous operations for long periods
\n
\n
\n
Critical
\n
0–25%
\n
It is sufficient to cause damage to the machine at any time
\n
\n\n
Table 12.
Variable “engine technical status”.
Source: Authors.
\n
In this context, the following groups of information and data are abstracted: the input values, called crisp, the linguistic variables, and the fuzzy variables. The fuzzy logic is justified in the solution of this case study in function of the input variables with better representation in fuzzy sets. The variables due to the dimension of the universe of study were divided in 04 (three) and 03 (two) inputs and 01 (one) output, all independent of each other.
\n
\n
4.1 The input variable “vibration analysis”
\n
For the determination of each variable, it was convenient to divide them into strips to approximate the actual situation to be checked. The calculation of these ranges on a scale according to Tables 6–12 is shown below.
\n
As the first level of variation, “vibration level” in Tables 6 and 7, let us consider better variable levels that were subdivided into four variables, normal, permissive, alert, and critical, each corresponding to the classification of vibration, velocity, and displacement levels measured in the equipment.
\n
\n
\n
4.2 The input variable lubricating oil
\n
The “level of analysis of the lubricating oil,” Tables 8 and 9, can be presented, for example, with the water content in the oil and solid and non-lubricated particle content (iron and copper), the energy sources of the dispatch of load for generation of energy. The levels of analysis of the command type were subdivided into 03 (three) variables, correspondence and information quality [9].
\n
\n
\n
4.3 The input variable “thermography analysis level”
\n
Thermography analysis is an input variable, Tables 10 and 11, that can be used as a tool for load dispatching. The levels of thermographic analysis were just been subdivided in four (4) variables, each one corresponding to the dynamic memory. The use of images in thermal plants is very important for this reason. The infrared radiation is a base of studies on the thermal images, which has a function of capturing this radiation, interpreting and generating a quantitative image of the temperature of the studied body [16].
\n
\n
\n
4.4 Output variable “technical condition of the motor”
\n
The “estimated technical state of the engine (ETM)” is the output variable of the system, in relation to vibration (oil, water, iron, and copper). Table 12 describes the operating state of the generating units. The variable under study, as well as the variable “level,” was transferred to a percentage scale of 100% where “GREET” corresponds to the range of maximum values and the variable “BAD” corresponds to the range of minimum values up to zero. This value gives a greater range of possibilities, making the case study more precise.
\n
\n
\n
\n
5. Fuzzy simulation
\n
The fuzzy inference with the input and output variables was performed employing the MATLAB version 8.0 tool and using a Mamdani model. This model adopts semantic rules for the processing of inferences and is commonly referred to as maximum-minimum inference. Such an inference model applies well to this type of problem since it uses union and intersection operations between sets. All variables are entered considering the intervals determined in the rules of inference. Figure 3 shows the variables “vibration,” “water,” thermography, “iron,” and “copper” according to Figure 3.
\n
Figure 3.
Mamdani’s model. Source: Authors.
\n
All variables are entered considering the intervals determined in the rules of inference. Figures 4–7 show the variables “vibration, water, thermography, iron, and copper” according to the figures below.
\n
Figure 4.
“Vibration level.” Source: Authors.
\n
Figure 5.
“Water.” Source: Authors.
\n
Figure 6.
“Thermography.” Source: Authors.
\n
Figure 7.
“Copper.” Source: Authors.
\n
The first input variable is a thermography (Figure 4). According to Tables 6 and 7, we have
\n
The second input variable is water (Figure 5) produced by the generating units. According to Tables 8 and 9, we have
\n
The third input variable is the thermography (Figure 6) produced by the generating units. According to Tables 10 and 11, we have
\n
The fourth input variable is iron (Figure 7) produced by the generating units. According to Tables 8 and 9, we have
\n
The fifth input variable is copper (Figure 8) produced by the generating units. According to Tables 8 and 9, we have
\n
Figure 8.
“Copper.” Source: Authors.
\n
The motor technical state is a product of the relationship between the input variable and output variable, which compose the pertinence functions expressed in the curves of Figure 9.
After editing the pertinence functions of all variables, the implemented rules are arranged in Figure 10, as shown in Figure 8 for the visualization of the linguistic variables, thus forming antecedents and subsequent ones based on the Fuzzy inference rules .
\n
Figure 10.
Implemented inference rules. Source: Authors.
\n
To better understand the screen expressed, Figure 11 shows all the possibilities that the simulation can produce. The movement of the red lines determines the other rule to be evaluated.
\n
Figure 11.
The input and output variables. Source: Authors.
\n
Figures 12–15 show the results of the inference rules from the 3D surface of the graph. In the blank, all the forms of execution are present that can exist within the simulation.
\n
Figure 12.
(Thermography × vibration). Source: Authors.
\n
Figure 13.
(Water vs. vibration). Source: Authors.
\n
Figure 14.
(Iron × vibration). Source: Authors.
\n
Figure 15.
(Copper vs. vibration). Source: Authors.
\n
\n
\n
6. Case study (fuzzy logic with predictive maintenance).
\n
\n
6.1 Vibration analysis
\n
Equipment status control is performed based on a calculated global value for the vibration signal measured at critical points on the machine surface. Since this value is due to a response signal from the structure to the dynamic excitation of the equipment operation, it represents a measure of the amplitude level of its vibration signal. In the case of the application for predictive maintenance, the international technical standards, among them the ISO, define two criteria for adoption of a global value (Figure 16).
\n
Figure 16.
Measurement points in the vibration analysis. Source: Authors.
\n
\n
\n
6.2 Analysis of water content in lubricating oil
\n
The determination of the presence and content of water in the case study was carried out through the distillation by drag. The sample is subjected to heating for distillation under controlled conditions, thus verifying the water content in the lubricating oil.
\n
The graph shows the results of the analysis of water content, done periodically as predictive, showing normal levels, since the tolerable content is 0.3% (Figure 17).
\n
Figure 17.
Water content and lubricating oil. Source: Authors.
\n
\n
6.2.1 Analysis of metal content in lubricating oil
\n
The graph made by direct reading (iron and copper) ferrography, which was carried out based on the extraction of the magnetizable contaminant particles, is contained in the lubricant, through the action of a magnetic field (Figures 18 and 19).
\n
Figure 18.
Copper content in the lubricating oil. Source: Authors.
\n
Figure 19.
Iron content in the lubricating oil. Source: Authors.
\n
\n
\n
\n
6.3 Thermography
\n
In addition to the use of the supervision system provided by the 9 FLUKE software, a thermovision is used, as shown in the figure, for measurement in low or high voltage electrical systems, temperature variations caused by excess electric current in the furnace motor/generator 01 be with the hot spot and it will not be able to pre-dispatch cargo, Figure 20.
\n
Figure 20.
Copper content in lubricating oil. Source: Authors.
\n
\n
6.3.1 Fuzzy logic goes into the operating conditions of the equipment
\n
In Figure 21, we can identify the anomalies likely to occur in the electric motor of generator 1 for all effects of temperature caused by excessive electric current. The heating screen of Figure 22 indicates that the engine/generator 1 cannot be related for preloading under operating plants. Other motors and generators are in the normal comfort area (A) and can be classified for normal operation of the diffuse rule such as the motors and tuners 2, 3, 4, 5, 6, 7, 8, 9, and 10. 5 and 25 mark as normal operations without interruption and execution of restriction only for the motor/generator 1. Activate the excluded points in your electronic connection.
\n
Figure 21.
Copper content in the lubricating oil. Source: Authors.
\n
Figure 22.
Copper content in the lubricating oil. Source: Authors.
\n
In Figure 21 the parameters for the location of the equipment, according to the engine/generator 1, are not allowed to operate because they are not in good operating condition. The other engines and generators are located in area A (N) normal 2, 3, 4, 5, 6, 7, 8, 9, and 10 and are able to position themselves according to the needs of the organization. Figure 22 informs which engines are conditional ideal for pre-shipment of cargo under operating conditions.
\n
\n
\n
\n
\n
7. Results achieved
\n
The objective of this work was to analyze the maintenance management system and its optimization through nebulous logic for the development of an intelligent system of support and decision-making for an ideal load dispatch demand.
\n
The interface of the developed computational tool achieved the simplicity desired by the users themselves, as well as the ease of learning in their operation. According to the facts presented in this paper, it was possible to show that, currently, a predictive maintenance program and a total maintenance program are indispensable for large companies. This is to provide reliability to processes and equipment, detecting problems still in the initial phase. Programs of this type provide good maintenance planning for the maintenance industry. Thus, the company grows with regard to meeting deadlines, resulting in an increase in customer satisfaction.
\n
In the present study, the gains from the two plans mentioned above could be assessed based on the information from the case study; we verified the reduction of corrective maintenance, and we verified the results with the increase of the MTBF and the decrease of the MTTR. The observed case can be implemented in any power generation machine that uses the fuel oil and, consequently, the use of oil stock, independent of the tank capacity and storage tank scales, which have only the standards of this system. The case study presented here can be implemented in any thermoelectric plant, independent of the loads to be dispatched, since the variables of this system are common to all.
\n
\n
Acknowledgments
\n
The authors gratefully acknowledge the support of this research by FAPEAM, UFPA, ELETROBRAS, and ITEGAM.
\n
\n',keywords:"economic load dispatch, management maintenance program, electric generators, power plants and fuzzy logic",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/66779.pdf",chapterXML:"https://mts.intechopen.com/source/xml/66779.xml",downloadPdfUrl:"/chapter/pdf-download/66779",previewPdfUrl:"/chapter/pdf-preview/66779",totalDownloads:436,totalViews:0,totalCrossrefCites:0,dateSubmitted:"July 13th 2018",dateReviewed:"November 29th 2018",datePrePublished:"April 18th 2019",datePublished:"January 22nd 2020",dateFinished:"April 18th 2019",readingETA:"0",abstract:"The development of a computational tool to support the decision of load dispatch according to the operational conditions of motors and generators of power plants is proposed, which are classified in relation to the probabilities of faults by a fuzzy system developed in this text, from indicators obtained from the analysis of lubricating oil, vibration analysis, and thermography of power generation equipment. The basis for the study is based on the principle of operation and operational conditions of the equipment to be dispatched for generation in a power plant, in addition to its particularities as specific consumption and the polluting emission for each equipment. In this way, this work aims not only to provide the tools to monitor these equipment but also, based on the management reports of vibration, temperature, and oil analysis, take corrective actions to maintain the necessary reliability and achieve the quality of the service through a preclearance procedure that takes into account the operating conditions of the equipment, obtaining performance indicators of the plan.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/66779",risUrl:"/chapter/ris/66779",signatures:"Milton Fonseca Junior, Antônio Estanislau Sanches, Jandecy Cabral Leite, Jorge de Almeida Brito Júnior, Manoel Henrique Reis Nascimento, Carlos Alberto Oliveira de Freitas, Tirso Lorenzo Reyes Carvajal, David Barbosa de Alencar, Jorge Laureano Moya Rodriguez, Ubiratan Holanda Berzerra and Nadime Mustafa Moraes",book:{id:"8623",title:"Maintenance Management",subtitle:null,fullTitle:"Maintenance Management",slug:"maintenance-management",publishedDate:"January 22nd 2020",bookSignature:"Fausto Pedro García Márquez and Mayorkinos Papaelias",coverURL:"https://cdn.intechopen.com/books/images_new/8623.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-83968-514-9",printIsbn:"978-1-83968-513-2",pdfIsbn:"978-1-83968-515-6",editors:[{id:"22844",title:"Prof.",name:"Fausto Pedro",middleName:null,surname:"García Márquez",slug:"fausto-pedro-garcia-marquez",fullName:"Fausto Pedro García Márquez"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"22303",title:"Dr.",name:"Ubiratan",middleName:"Holanda",surname:"Holanda Berzerra",fullName:"Ubiratan Holanda Berzerra",slug:"ubiratan-holanda-berzerra",email:"bira@ufpa.br",position:null,institution:{name:"Federal University of Para",institutionURL:null,country:{name:"Brazil"}}},{id:"229124",title:"Dr.",name:"Jorge",middleName:"De Almeida Brito",surname:"de Almeida Brito Júnior",fullName:"Jorge de Almeida Brito Júnior",slug:"jorge-de-almeida-brito-junior",email:"jorge.brito.jr.83@gmail.com",position:null,institution:null},{id:"230306",title:"Dr.",name:"Manoel Henrique",middleName:null,surname:"Reis Nascimento",fullName:"Manoel Henrique Reis Nascimento",slug:"manoel-henrique-reis-nascimento",email:"hreys@itegam.org.br",position:null,institution:null},{id:"230308",title:"Dr.",name:"Jandecy",middleName:null,surname:"Cabral Leite",fullName:"Jandecy Cabral Leite",slug:"jandecy-cabral-leite",email:"jandecycabral@hotmail.com",position:null,institution:null},{id:"230312",title:"Dr.",name:"Jorge Laureano",middleName:null,surname:"Moya Rodriguez",fullName:"Jorge Laureano Moya Rodriguez",slug:"jorge-laureano-moya-rodriguez",email:"jorgemoyar@gmail.com",position:null,institution:null},{id:"230316",title:"MSc.",name:"Carlos Alberto",middleName:"Alberto Oliveira De",surname:"Oliveira de Freitas",fullName:"Carlos Alberto Oliveira de Freitas",slug:"carlos-alberto-oliveira-de-freitas",email:"caofreitas@yahoo.com.br",position:null,institution:null},{id:"230317",title:"Dr.",name:"Milton",middleName:null,surname:"Fonseca Júnior",fullName:"Milton Fonseca Júnior",slug:"milton-fonseca-junior",email:"milton.fonseca.jr@gmail.com",position:null,institution:null},{id:"243299",title:"MSc.",name:"David Barbosa",middleName:null,surname:"de Alencar",fullName:"David Barbosa de Alencar",slug:"david-barbosa-de-alencar",email:"david002870@hotmail.com",position:null,institution:null},{id:"243300",title:"Dr.",name:"Nadime Mustafa",middleName:null,surname:"Moraes",fullName:"Nadime Mustafa Moraes",slug:"nadime-mustafa-moraes",email:"mustafanadime@gmail.com",position:null,institution:null},{id:"243302",title:"Dr.",name:"Tirso Lorenzo Reyes",middleName:null,surname:"Carvajal",fullName:"Tirso Lorenzo Reyes Carvajal",slug:"tirso-lorenzo-reyes-carvajal",email:"tirsolrca@gmail.com",position:null,institution:null},{id:"272584",title:"Dr.",name:"Antônio",middleName:null,surname:"Estanislau Sanches",fullName:"Antônio Estanislau Sanches",slug:"antonio-estanislau-sanches",email:"novo.sanches@gmail.com",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Maintenance systems and their application in thermoelectric plants",level:"1"},{id:"sec_3",title:"3. Fragments used for implant and end methodology TPM program",level:"1"},{id:"sec_3_2",title:"3.1 Pillar of specific improvements (recommended group: coordinators of ME, MA (plant managers), MP, and SMA)",level:"2"},{id:"sec_4_2",title:"3.2 Automatic maintenance pillar (recommended group: managers, supervisors, and operators of each plant)",level:"2"},{id:"sec_5_2",title:"3.3 Planned maintenance pillar (recommended group: PM coordinator, service managers and supervisors, and each plant)",level:"2"},{id:"sec_6_2",title:"3.4 Pillar of education and training (recommended group: this pillar is corporate and only depends on HR)",level:"2"},{id:"sec_8",title:"4. Predictive maintenance using computational (fuzzy logic) decision support tool in preload dispatch",level:"1"},{id:"sec_8_2",title:"4.1 The input variable “vibration analysis”",level:"2"},{id:"sec_9_2",title:"4.2 The input variable lubricating oil",level:"2"},{id:"sec_10_2",title:"4.3 The input variable “thermography analysis level”",level:"2"},{id:"sec_11_2",title:"4.4 Output variable “technical condition of the motor”",level:"2"},{id:"sec_13",title:"5. Fuzzy simulation",level:"1"},{id:"sec_14",title:"6. Case study (fuzzy logic with predictive maintenance).",level:"1"},{id:"sec_14_2",title:"6.1 Vibration analysis",level:"2"},{id:"sec_15_2",title:"6.2 Analysis of water content in lubricating oil",level:"2"},{id:"sec_15_3",title:"6.2.1 Analysis of metal content in lubricating oil",level:"3"},{id:"sec_17_2",title:"6.3 Thermography",level:"2"},{id:"sec_17_3",title:"6.3.1 Fuzzy logic goes into the operating conditions of the equipment",level:"3"},{id:"sec_20",title:"7. Results achieved",level:"1"},{id:"sec_21",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Silva SG. Sistema interligado nacional: análise das penalizações impostas às transmissoras com foco na aplicação da parcela variável. Dissertação (Mestrado em Ciências)-Universidade Federal de Uberlândia, Uberlândia. 2016. p. 122\n'},{id:"B2",body:'Ma H et al. Multi-objective biogeography-based optimization for dynamic economic emission load dispatch considering plug-in electric vehicles charging. Energy. 2017;135:101-111. ISSN 0360-5442\n'},{id:"B3",body:'Pless J, Fell H. Bribes, bureaucracies, and blackouts: Towards understanding how corruption at the firm level impacts electricity reliability. Resource and Energy Economics. 2017;47:36-55. ISSN 0928-7655\n'},{id:"B4",body:'Kumar N. A non convex cost function based optimal load dispatch using tlbo algorithm. Journal of Engineering Science & Technology Review. 2017;10(1). ISSN 1791-2377\n'},{id:"B5",body:'Alam N et al. A 0/1 mixed integer linear programming approach to establish an effective preventive maintenance policy for power plant. International Journal of Industrial and Systems Engineering. 2017;25(4):478-498\n'},{id:"B6",body:'Alaswad S, Xiang Y. A review on condition-based maintenance optimization models for stochastically deteriorating system. Reliability Engineering & System Safety. 2017;157:54-63\n'},{id:"B7",body:'Arya R. Ranking of feeder sections of distribution systems for maintenance prioritization accounting distributed generations and loads using diagnostic importance factor (DIF). International Journal of Electrical Power & Energy Systems. 2016;74:70-77\n'},{id:"B8",body:'Azadeh A, Asadzadeh SM, Salehi N, Firoozi M. Condition-based maintenance effectiveness for series–parallel power generation system—A combined Markovian simulation model. Reliability Engineering & System Safety. 2015;142:357-368\n'},{id:"B9",body:'Baidya R, Ghosh SK. Model for a predictive maintenance system effectiveness using the analytical hierarchy process as analytical tool. IFAC-PapersOnLine. 2015;48(3):1463-1468\n'},{id:"B10",body:'Ripper is a 2001 Canadian-British horror film, directed by John Eyres. The film stars A. J. Cook and Bruce Payne. It was written and produced by John A. Curtis\n'},{id:"B11",body:'Fonseca, Miltonx, et al. Pre-dispatch of load in thermoelectric power plants considering maintenance management using fuzzy logic. IEEE Access. 2018;6:41379-41390\n'},{id:"B12",body:'Junior MF et al. Maintenance tools applied to electric generators to improve energy efficiency and power quality of thermoelectric power plants. Energies. 2017;10(8):1091\n'},{id:"B13",body:'Larguech S et al. Fuzzy sliding mode control for turbocharged diesel engine. Journal of Dynamic Systems, Measurement, and Control. 2016;138(1):011009. ISSN 0022-0434\n'},{id:"B14",body:'Gonçalves BP et al. Avaliação de impactos harmônicos na rede elétrica através dos indicadores THD e fator de potência utilizando lógica fuzzy. Revista Brasileira de Energia. 2013;19(1):9-27\n'},{id:"B15",body:'Nogueira EL, Nascimento MHR. Inventory control applying sales demand prevision based on fuzzy inference system. ITEGAM-Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA). 2017;3:31-36. DOI: 10.5935/2447-0228.20170060\n'},{id:"B16",body:'López-Pérez D, Antonino-Daviu J. Application of infrared thermography to failure detection in industrial induction motors: Case stories. IEEE Transactions on Industry Applications. 2017;53(3):1901-1908\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Milton Fonseca Junior",address:"milton.fonseca.jr@gmail.com",affiliation:'
Engineering Department, Generation of Mauá, Eletrobrás Amazonas GT, Brazil
Department of Post Graduate in Electric Engineering, Federal University of Pará (UFPA), Brazil
'},{corresp:null,contributorFullName:"Nadime Mustafa Moraes",address:null,affiliation:'
University of the State of Amazon (UEA), Brazil
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