Publication

Abstract : The industrial sector relies heavily on induction motors. Because of the age of the machine in service, fault diagnosis and condition assessment (FDCA) of rotating machines becomes critical. The use of conventional methods generally suffer from inaccurate Induction Motor external faults identification. Condition monitoring of the motor are hence important to identify the faults and take precautions before the operation of induction motor get shut down. The proposed method adopts motor electrical signature analysis (MESA), a non-invasive method for the external fault identification and feature extraction. Multilabel classification of external faults was performed using various machine learning models. Ensemble bagged tree and Support Vector Machine (SVM) are identified as the most accurate classification model for data set with and without noise.