Vibration-Based Fault Diagnosis of Commutator Motor

Abstract EN

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.