Vibration‐Based Fault Diagnosis of Commutator Motor

This paper presents a study on vibration‐based fault diagnosis techniques of a commutator motor (CM). Proposed techniques used vibration signals and signal processing methods. The authors analysed recognition efficiency for 3 states of the CM: healthy CM, CM with broken tooth on sprocket, CM with broken rotor coil. Feature extraction methods called MSAF‐RATIO‐50‐SFC (method of selection of amplitudes of frequencies ratio 50 second frequency coefficient), MSAF‐RATIO‐50‐SFC‐EXPANDED were implemented and used for an analysis. Feature vectors were obtained using MSAF‐RATIO‐50‐SFC, MSAF‐RATIO‐50‐SFC‐EXPANDED, and sum of RSoV. Classification methods such as nearest mean (NM) classifier, linear discriminant analysis (LDA), and backpropagation neural network (BNN) were used for the analysis. A total efficiency of recognition was in the range of 79.16%–93.75% (TV). The proposed methods have practical application in industries.