Acoustic based fault diagnosis of three-phase induction motor

Abstract

The article describes acoustic based fault diagnosis techniques of a three-phase induction motor. Four real states of the three-phase induction motor were analysed: healthy three-phase induction motor, three-phase induction motor with broken rotor bar, three-phase induction motor with 2 broken rotor bars, three-phase induction motor with faulty ring of squirrel-cage. Two feature extraction methods of acoustic signals of the induction motor - SMOFS-32-MULTIEXPANDED-2-GROUPS (Shortened Method of Frequencies Selection Multiexpanded 2 Groups) and SMOFS-32-MULTIEXPANDED-1-GROUP were described. The Nearest Neighbour classifier, backpropagation neural network and modified classifier based on words coding were used for recognition of acoustic signals. Results of recognition were very good for the real data and developed fault diagnosis techniques based on acoustic signals. The described fault diagnosis approach can find applications in the industry.