Reprint

Information Theory and Its Application in Machine Condition Monitoring

Edited by
March 2022
194 pages
  • ISBN978-3-0365-3208-0 (Hardback)
  • ISBN978-3-0365-3209-7 (PDF)

This is a Reprint of the Special Issue Information Theory and Its Application in Machine Condition Monitoring that was published in

Chemistry & Materials Science
Computer Science & Mathematics
Physical Sciences
Summary

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
  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
Keywords
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; fault detection; optimal bandwidth; kernel density estimation; JS divergence; bearing; domain adaptation; partial transfer; fault diagnosis; 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; fault diagnosis; rotating machinery; transfer learning; domain adaptation; satellite momentum wheel; anomaly detection; Huffman-multi-scale entropy (HMSE); support vector machine (SVM); adaptive particle swarm optimization (APSO); deep learning; rail surface defect detection; machine vision; YOLOv4; MobileNetV3; deep learning; fault diagnosis; multi-source heterogeneous fusion; gearbox; transfer learning; n/a

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