Most of the mechanical systems in industries are made to run through induction motors (IM).\nTo maintain the performance of the IM, earlier detection of minor fault and continuous monitoring\n(CM) are required. Among IM faults, bearing faults are considered as indispensable because of its\nhigh probability incidence nature. CM mainly depends upon signal processing and fault detection\ntechniques. In recent decades, various methods have been involved in detecting the bearing fault\nusing machine learning (ML) algorithms. Additionally, the role of artificial intelligence (AI), a growing\ntechnology, has also been used in fault diagnosis of IM. Taking the necessity of minor fault detection\nand the detailed study about the role of ML and AI to detect the bearing fault, the present study is\nperformed. A comprehensive study is conducted by considering various diagnosis methods from ML\nand AI for detecting a minor bearing fault (hole and scratch). This study helps in understanding the\ndierence between the diagnosis approach and their effectiveness in detecting an IM bearing fault.\nIt is accomplished through FFT (fast Fourier transform) analysis of the load current and the extracted\nfeatures are used to train the algorithm. The application is extended by comparing the result of ML\nand AI, and then explaining the specific purpose of use.
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