Affiliation:
1. School of Mechanical & Electrical Engineering, Guizhou Normal University, Guiyang 550001, China
Abstract
This paper proposes a fault diagnosis method for miniature DC motors (MDCMs) in the presence of the uncertainties caused by material and random factors of the production process. In this method, the probability models of fault multiple features are established based on the advantage criterion of the maximum overall average membership to determine the distribution of fault multiple features. The fault diagnosis algorithm is synthesized to obtain the threshold ranges of fault multiple features according to different confidence levels. Experimental test results are presented and analyzed to validate the efficiency and performance of the proposed fault diagnosis method.
Funder
Guizhou Normal University
Subject
Mechanical Engineering,Mechanics of Materials,Geotechnical Engineering and Engineering Geology,Condensed Matter Physics,Civil and Structural Engineering
Cited by
1 articles.
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