A neural network approach to real-time condition monitoring of induction motors

A neural network-based incipient fault detector for small and medium-size induction motors is developed. The detector avoids the problems associated with traditional incipient fault detection schemes by employing more readily available information such as rotor speed and stator current. The neural n...

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Bibliographic Details
Published inIEEE transactions on industrial electronics (1982) Vol. 38; no. 6; pp. 448 - 453
Main Authors Chow, M.-y., Mangum, P.M., Yee, S.O.
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.12.1991
Institute of Electrical and Electronics Engineers
Subjects
A.c. Machines
Applied sciences
Artificial neural networks
Condition monitoring
Electrical engineering. Electrical power engineering
Electrical fault detection
Electrical machines
Exact sciences and technology
Fault detection
Induction motors
Laboratories
Neural networks
Rotating machines
Rotors
Stator windings
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ISSN0278-0046
DOI10.1109/41.107100

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Summary:A neural network-based incipient fault detector for small and medium-size induction motors is developed. The detector avoids the problems associated with traditional incipient fault detection schemes by employing more readily available information such as rotor speed and stator current. The neural network design is evaluated in real time in the laboratory on a 3/4 hp permanent magnet induction motor. The results of this evaluation indicate that the neural-network-based incipient fault detector provides a satisfactory level of accuracy, greater than 95%, which is suitable for real-world applications.< >
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ISSN:0278-0046
DOI:10.1109/41.107100