Soft computing methods in motor fault diagnosis

• Published in: Applied soft computing Vol. 1; no. 1; pp. 73 - 81
• Main Authors: Gao, X.Z., Ovaska, S.J.
• Format: Journal Article
• Language: English
• Published: 01-06-2001
• ISSN: 1568-4946
• DOI: 10.1016/S1568-4946(01)00008-4

During the last decade, soft computing (computational intelligence) has attracted great interest from different areas of research. In this paper, we give an overview on the recent developments in the emerging field of soft computing-based electric motor fault diagnosis. Several typical fault diagnosis schemes using neural networks, fuzzy logic, neural-fuzzy, and genetic algorithms, with descriptive diagrams as well as simplified algorithms are presented. Their advantages and disadvantages are compared and discussed. We conclude that soft computing methods have great potential in dealing with difficult fault detection and diagnosis problems.