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Fault Diagnosis of Electromotor Acoustically Using Machine Learning Approach

Article Type Original Research One of the methods used to minimize the cost of maintaining and repairing rotating industrial equipment is condition monitoring by sound analysis. This study was performed to diagnose the fault of a single-phase electromotor through a machine learning method aiming to monitor its condition by sound analysis. Test conditions included healthy mode, bearing failure, shaft imbalance and shaft wear at two speeds of 500 and 1400 rpm. A microphone was installed on the electromotor to record data. After data collection, signal processing and statistical analysis, the data were clustered by machine learning method and K mean algorithm and the best characteristics were selected by PCA method. These features were used in the ANFIS modeling process. These features were common to both electromotor speeds. 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. Authors Vafa Samadi V.1 Mostafa Mostafaei M.2 Ali Nejat Lorestani A.N.2 How to cite this article Samadi V, Mostafaei M, Lorestani A N. Fault Diagnosis of Electromotor Acoustically Using Machine Learning Approach, Modares Mechanical Engineering. Modares Mechanical Engineering.2021;21(8):563-573.