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Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems

Edited by
February 2023
200 pages
  • ISBN978-3-0365-6462-3 (Hardback)
  • ISBN978-3-0365-6463-0 (PDF)
https://doi.org/10.3390/books978-3-0365-6463-0
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This book is a reprint of the Special Issue Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems that was published in

Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Summary

Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industries.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
defect detection; Directional Nature of Fault; gravure printing; fault diagnosis; roll-to-roll printed electronics; sensor data characterization; landing gear retraction/extension(R/E) system; 1-D dilated convolutional neural network (1-DDCNN); fault diagnosis; feature integration; rub-impact; tip clearance; shape memory alloy; aero-engine; fault mitigation; vibration energy harvesting; vibration sensor; self-powered sensor; clean technology; wireless vibration sensor; IoT support technology; partial transfer learning; ensemble strategy; fault diagnosis; deep adversarial convolutional neural network; mechanical fault diagnosis; unbalanced data set; MeanRadius-SMOTE; minority class; incipient fault detection; robustness; reinforcement learning; anomaly detection; rolling bearings; fault diagnosis; piecewise aggregate approximation; CEEMDAN; actuator and sensor faults; TS fuzzy system; sliding mode observer (SMO); H∞ performance; non-quadratic Lyapunov function (NQLF); fmincon; fault reconstruction; water distribution network; vibro-acoustic sensors; leak detection; structural health monitoring; feature extraction; signal processing; machine learning; binary classification; data-driven; neural network; composite multiscale transition permutation entropy; bearing; fault diagnosis; feature extraction; n/a