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.
Highlights
Due to their small size, lightweight, and easy control, miniature DC motors (MDCMs) are widely used in the industrial fields of home appliances, office automation, automotive parts, etc. [1]
The factory quality inspection of MDCMs mainly relies on the manual experience, which leads to low production efficiency, heavy workload, and missed inspection [2]. erefore, the effective motor fault detection technology on the production line is crucial for the factory quality of MDCMs
(2) Designing a fault diagnosis algorithm based on probability modeling to deal with the uncertainties caused by material and random factors of the production process, improving the accuracy of MDCM fault diagnosis
Summary
Due to their small size, lightweight, and easy control, miniature DC motors (MDCMs) are widely used in the industrial fields of home appliances, office automation, automotive parts, etc. [1]. Is paper proposes a novel solution to apply a fault diagnosis method for the MDCM by incorporating the probability modeling and the advantage criterion of the maximum overall average membership. (1) Proposing the advantage criterion of the maximum overall average membership to determine the optimal fitting distribution, which cannot be handled by the traditional hypothesis test (2) Designing a fault diagnosis algorithm based on probability modeling to deal with the uncertainties caused by material and random factors of the production process, improving the accuracy of MDCM fault diagnosis (3) Designing the diagnostic test platform of MDCMs to validate the performance of the proposed fault diagnosis method e rest of this paper is arranged as follows: in Section 2, the structure of the MDCM and data sources are described in detail.
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