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Fault Diagnosis, Prognosis, and Reliability for Electrical Machines and Drives [#330170]



Fault Diagnosis, Prognosis, and Reliability for Electrical Machines and Drives
English | 2021 | ISBN: 1119722756 , 978-1119722755 | 445 pages | True PDF | 51.42 MB
An insightful treatment of present and emerging technologies in fault diagnosis and failure prognosis


InFault Diagnosis, Prognosis, and Reliability for Electrical Machines and Drives,a team of distinguished researchers deliversa comprehensive exploration of current and emerging approaches tofault diagnosis and failure prognosis of electrical machines and drives. The authors begin withfoundational background, describing the physics of failure, the motor and drive designs and components that affect failure and signals, signal processing, and analysis.
The book then moves on to describe the features of these signals and the methods commonly used to extract these features to diagnose the health of a motor or drive, as well as the methods used to identify the state of health and differentiate between possible faultsortheir severity.
Fault Diagnosis, Prognosis, and Reliability for Electrical Machines and Drivesdiscusses the tools used to recognize trends towards failure andthe estimation of remaining useful life. It addresses the relationships between fault diagnosis, failure prognosis, and fault mitigation.
The bookalso provides:
  • A thorough introduction to the modes of failure, how early failure precursors manifest themselves in signals, and how features extracted from these signals are processed
  • A comprehensive exploration of the fault diagnosis, the results of characterization, andhow they used to predict the time of failure and the confidence interval associated with it
  • A focus on medium-sized drives, including induction, permanent magnet AC, reluctance, and new machine and drive types

Perfect forresearchers and students who wish to study or practice in the rea of electrical machines and drives,Fault Diagnosis, Prognosis, and Reliability for Electrical Machines and Drivesis also an indispensable resource for researchers with a background in signal processing or statistics.

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