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Over the last three decades, the search for competitiveness and
growth gains has driven the evolution of machine maintenance
policies, and the industry has moved from passive maintenance to
active maintenance with the aim of improving productivity. Active
maintenance requires continuous monitoring of industrial systems in
order to increase reliability, availability rates and guarantee the
safety of people and property. This book presents the main advanced
signal processing techniques for fault detection and diagnosis in
electromechanical systems. It focuses on presenting these advanced
tools from time-frequency representation and time-scale analysis to
demodulation techniques, including innovative and recently
developed options. Each technique is evaluated and compared, and
its advantages and drawbacks highlighted. Parametric spectral
analysis, which aims to handle some of the main drawbacks of these
approaches, is introduced as a potential solution. Signal
Processing for Fault Detection and Diagnosis in Electric Machines
and Systems offers thorough, analytical coverage of the following
topics: parametric signal processing approach; the signal
demodulation techniques; Kullback-Leibler divergence for incipient
fault diagnosis; high-order spectra (HOS); and fault detection and
diagnosis based on principal component analysis. Finally, a brief
conclusion suggests some possibilities for the future direction of
the field. The book is a useful resource for researchers and
engineers whose work involves electrical machines or fault
detection specifically, and also of value to postgraduate students
with an interest in entering this field.
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