Application of fuzzy data fusion theory in fault diagnosis of rotating machinery. (August 2018)

• Record Type: Journal Article
• Authors: Jafari, Hamideh; Poshtan, Javad; Sadeghi, Hamed
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 232:Number 8(2018)
• Page Start: 1015
• Page End: 1024
• Publication Date: 2018-08
• Subjects: Data fusion -- decentralized fuzzy integral -- induction motor; Mechanical engineering -- Periodicals; Automatic control -- Periodicals; Systems engineering -- Periodicals; 621.3
• DOI: 10.1177/0959651818772935
• Languages: English
• ISSNs: 0959-6518

Abstract

In this article, the most common induction motor faults including bearing outer race defect, broken rotor bar, and short-circuit of stator windings are diagnosed with high reliability. The decentralized fuzzy-integral data fusion method is used for information fusion in feature level. In the proposed scheme, the feature vectors are constructed using signatures created by time-domain characteristics obtained from stator three-phase current measurements. Partial matching of each feature is calculated by the fuzzy c-mean classifier algorithm, and features with high diagnosis ability are fused by Choquet fuzzy integral. The technique is validated experimentally on the 4 hp induction motor of an electropump, and the results are presented.