Fault Diagnosis of Electromotor Acoustically Using Machine Learning Approach

To minimize the cost of maintenance and repair of rotating industrial equipment, one of the methods used is condition monitoring by sound analysis. This study was performed to diagnose the fault of a single-phase electric motor through machine learning method aiming to monitor its situation by sound analysis. Test conditions included healthy state, bearing failure, shaft imbalance and shaft wear at two speeds of 500 and 1400 rpm. A microphone was installed on the electric motor to record data. After data acquisition, signal processing and statistical analysis, the best characteristics were selected by PCA method and then the data were clustered by machine learning method and K mean algorithm. These features used in the ANFIS modeling process were common features selected in both electromotor speed situations. After evaluating the models, the best model had the highest accuracy value of 96.82%. The average accuracy was 96.71% for overall fault classification. The results showed that the analysis of acoustic signals and modeling process can be used to diagnose electromotor defects by machine learning method. Based on the obtained results, condition monitoring of the electromotor through acoustic analysis reduces its stop and continues its work process in the industry. The repair costs of the electromotor are reduced by its proper condition monitoring.