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Motor Fault Detection and Diagnosis Using Soft Computing

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Soft Computing in Industrial Electronics

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 101))

Abstract

AC and DC motors are intensively applied in various industrial applications [1]. Changing working environment and dynamical loading always strain and wear motors and cause incipient faults such as shorted turns, broken bearings, and damaged rotor bars [2]. These faults can result in serious performance degradation and eventual system failures, if they are not properly detected and handled. Improved safety and reliability can be achieved with appropriate early fault diagnosis strategies leading to the concept of preventive maintenance. Furthermore, great maintenance costs are saved by applying advanced detection methods to find those developing failures. Motor drive monitoring, fault detection and diagnosis are, therefore, very important and challenging topics in the electrical engineering field [3].

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  1. Helsinki University of Technology, Finland

    Xiao-Zhi Gao & Seppo J. Ovaska

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  1. Xiao-Zhi Gao
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  2. Seppo J. Ovaska
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© 2002 Springer-Verlag Berlin Heidelberg

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Gao, XZ., Ovaska, S.J. (2002). Motor Fault Detection and Diagnosis Using Soft Computing. In: Soft Computing in Industrial Electronics. Studies in Fuzziness and Soft Computing, vol 101. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1783-6_1

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  • DOI: https://doi.org/10.1007/978-3-7908-1783-6_1

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-2513-8

  • Online ISBN: 978-3-7908-1783-6

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