Signal Processing for Fault Detection and Diagnosis in Electric Machines and Systems presents a comprehensive overview of advanced techniques for monitoring and diagnosing electromechanical systems. Over the last three decades, industry has shifted from passive to active maintenance strategies, emphasizing continuous monitoring to improve reliability, availability, and safety.
This book focuses on state-of-the-art signal processing methods, from time-frequency representations and time-scale analyses to demodulation techniques, including innovative and recently developed approaches. Each technique is critically evaluated, with advantages and limitations discussed. Parametric spectral analysis is presented as a promising solution to some of the main challenges in fault detection.
Key topics covered include:
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Parametric signal processing methods
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Signal demodulation techniques
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Kullback-Leibler divergence for incipient fault diagnosis
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High-order spectra (HOS) analysis
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Fault detection using principal component analysis (PCA)
Designed for researchers, engineers, and postgraduate students, this work provides both theoretical insights and practical guidance for fault detection and diagnosis in electrical machines and systems, as well as directions for future developments in the field.
