Using a neural/fuzzy system to extract heuristic knowledge of incipient faults in induction motors. Part I-Methodology

• Published in: IEEE transactions on industrial electronics (1982) Vol. 42; no. 2; pp. 131 - 138
• Main Authors: Goode, P.V., Mo-yuen Chow
• Format: Journal Article
• Language: English
• Published: IEEE 01.04.1995
• Subjects: Artificial neural networks; Condition monitoring; Degradation; Electric motors; Fault detection; Fuzzy logic; Fuzzy systems; Knowledge engineering; Neural networks; Reliability engineering
• ISSN: 0278-0046
• DOI: 10.1109/41.370378

The use of electric motors in industry is extensive. These motors are exposed to a wide variety of environments and conditions which age the motor and make it subject to incipient faults. These incipient faults, if left undetected, contribute to the degradation and eventual failure of the motors. Artificial neural networks have been proposed and have demonstrated the capability of solving the motor monitoring and fault detection problem using an inexpensive, reliable, and noninvasive procedure. However, the major drawback of conventional artificial neural network fault detection is the inherent black box approach that can provide the correct solution, but does not provide heuristic interpretation of the solution. Engineers prefer accurate fault detection as well as the heuristic knowledge behind the fault detection process. Fuzzy logic is a technology that can easily provide heuristic reasoning while being difficult to provide exact solutions. The authors introduce the methodology behind a novel hybrid neural/fuzzy system which merges the neural network and fuzzy logic technologies to solve fault detection problems. They also discuss a training procedure for this neural/fuzzy fault detection system. This procedure is used to determine the correct solutions while providing qualitative, heuristic knowledge about the solutions.< >