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Application of AI tools in fault diagnosis of electrical machines and drives-an overview

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2 Author(s)
M. A. Awadallah ; Dept. of Electr. & Comput. Eng., Kansas State Univ., Manhattan, KS, USA ; M. M. Morcos

Condition monitoring leading to fault diagnosis and prediction of electrical machines and drives has recently become of importance. The topic has attracted researchers to work in during the past few years because of its great influence on the operational continuation of many industrial processes. Correct diagnosis and early detection of incipient faults result in fast unscheduled maintenance and short down time for the machine under consideration. It also avoids harmful, sometimes devastative, consequences and helps reduce financial loss. Reduction of the human experts involvement in the diagnosis process has gradually taken place upon the recent developments in the modern artificial intelligence (AI) tools. Artificial neural networks (ANNs), fuzzy and adaptive fuzzy systems, and expert systems are good candidates for the automation of the diagnostic procedures. This present work surveys the principles and criteria of the diagnosis process. It introduces the current research achievements to apply AI techniques in the diagnostic systems of electrical machines and drives.

Published in:

IEEE Transactions on Energy Conversion  (Volume:18 ,  Issue: 2 )