Sensors Fault Diagnosis Trends and Applications

doi:10.3390/books978-3-0365-1049-1

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Sensors Fault Diagnosis Trends and Applications

Edited by

July 2021

236 pages

ISBN978-3-0365-1048-4 (Hardback)
ISBN978-3-0365-1049-1 (PDF)

https://doi.org/10.3390/books978-3-0365-1049-1

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This book is a reprint of the Special Issue Sensors Fault Diagnosis Trends and Applications that was published in

Chemistry & Materials Science

Engineering

Environmental & Earth Sciences

Summary

Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis.

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Hardback

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Keywords

rolling bearing; performance degradation; hybrid kernel function; krill herd algorithm; SVR; acoustic-based diagnosis; gear fault diagnosis; attention mechanism; convolutional neural network; stacked auto-encoder; weighting strategy; deep learning; bearing fault diagnosis; intelligent leak detection; acoustic emission signals; statistical parameters; support vector machine; wavelet denoising; Shannon entropy; adaptive noise reducer; gaussian reference signal; gearbox fault diagnosis; one against on multiclass support vector machine; varying rotational speed; fault detection and diagnosis; faults estimation; actuator and sensor fault; observer design; Takagi-Sugeno fuzzy systems; automotive; perception sensor; lidar; fault detection; fault isolation; fault identification; fault recovery; fault diagnosis; fault detection and isolation (FDIR); autonomous vehicle; model predictive control; path tracking control; fault detection and isolation; braking control; nonlinear systems; fault tolerant control; fault detection; iterative learning control; neural networks; cryptography; wireless sensor networks; machine learning; scan-chain diagnosis; fault detection and diagnosis; artificial neural network; NARX; control valve; decision tree; signature matrix; n/a