Information Theory and Its Application in Machine Condition Monitoring
Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a...
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| Formatua: | Online |
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| Hizkuntza: | ingelesa |
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MDPI - Multidisciplinary Digital Publishing Institute
2022
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| Sarrera elektronikoa: | ONIX_20220506_9783036532080_152 |
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| _version_ | 1869531183780462592 |
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| collection | Directory of Open Access Books |
| description | Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries. |
| format | Online |
| id | doab-20.500.12854ir-81086 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-810862024-04-11T15:10:27Z Information Theory and Its Application in Machine Condition Monitoring Li, Yongbo Gu, Fengshou Liang, Xihui fault detection deep learning transfer learning anomaly detection bearing wind turbines misalignment fault diagnosis information fusion improved artificial bee colony algorithm LSSVM D–S evidence theory optimal bandwidth kernel density estimation JS divergence domain adaptation partial transfer subdomain rotating machinery gearbox signal interception peak extraction cubic spline interpolation envelope combined fault diagnosis empirical wavelet transform grey wolf optimizer low pass FIR filter support vector machine satellite momentum wheel Huffman-multi-scale entropy (HMSE) support vector machine (SVM) adaptive particle swarm optimization (APSO) rail surface defect detection machine vision YOLOv4 MobileNetV3 multi-source heterogeneous fusion n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries. 2022-05-06T11:26:53Z 2022-05-06T11:26:53Z 2022 book ONIX_20220506_9783036532080_152 9783036532080 9783036532097 https://directory.doabooks.org/handle/20.500.12854/81086 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/5114 https://mdpi.com/books/pdfview/book/5114 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-3209-7 10.3390/books978-3-0365-3209-7 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036532080 9783036532097 194 Basel open access |
| spellingShingle | fault detection deep learning transfer learning anomaly detection bearing wind turbines misalignment fault diagnosis information fusion improved artificial bee colony algorithm LSSVM D–S evidence theory optimal bandwidth kernel density estimation JS divergence domain adaptation partial transfer subdomain rotating machinery gearbox signal interception peak extraction cubic spline interpolation envelope combined fault diagnosis empirical wavelet transform grey wolf optimizer low pass FIR filter support vector machine satellite momentum wheel Huffman-multi-scale entropy (HMSE) support vector machine (SVM) adaptive particle swarm optimization (APSO) rail surface defect detection machine vision YOLOv4 MobileNetV3 multi-source heterogeneous fusion n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Information Theory and Its Application in Machine Condition Monitoring |
| title | Information Theory and Its Application in Machine Condition Monitoring |
| title_full | Information Theory and Its Application in Machine Condition Monitoring |
| title_fullStr | Information Theory and Its Application in Machine Condition Monitoring |
| title_full_unstemmed | Information Theory and Its Application in Machine Condition Monitoring |
| title_short | Information Theory and Its Application in Machine Condition Monitoring |
| title_sort | information theory and its application in machine condition monitoring |
| topic | fault detection deep learning transfer learning anomaly detection bearing wind turbines misalignment fault diagnosis information fusion improved artificial bee colony algorithm LSSVM D–S evidence theory optimal bandwidth kernel density estimation JS divergence domain adaptation partial transfer subdomain rotating machinery gearbox signal interception peak extraction cubic spline interpolation envelope combined fault diagnosis empirical wavelet transform grey wolf optimizer low pass FIR filter support vector machine satellite momentum wheel Huffman-multi-scale entropy (HMSE) support vector machine (SVM) adaptive particle swarm optimization (APSO) rail surface defect detection machine vision YOLOv4 MobileNetV3 multi-source heterogeneous fusion n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | fault detection deep learning transfer learning anomaly detection bearing wind turbines misalignment fault diagnosis information fusion improved artificial bee colony algorithm LSSVM D–S evidence theory optimal bandwidth kernel density estimation JS divergence domain adaptation partial transfer subdomain rotating machinery gearbox signal interception peak extraction cubic spline interpolation envelope combined fault diagnosis empirical wavelet transform grey wolf optimizer low pass FIR filter support vector machine satellite momentum wheel Huffman-multi-scale entropy (HMSE) support vector machine (SVM) adaptive particle swarm optimization (APSO) rail surface defect detection machine vision YOLOv4 MobileNetV3 multi-source heterogeneous fusion n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | ONIX_20220506_9783036532080_152 |