Deep Learning Based Fault Diagnosis of Electrical and Mechanical Faults in Induction Motor

Abstract

The induction motor (IM) is a supreme significant motor in the industry, and demand for its dependability and safety is increasing by the day. They are dependable, but if not properly maintained, they might wear out prematurely, resulting in severe financial and manpower losses. This encourages us in the direction of creating an intellectual approach for detecting initial induction motor errors. The reason for this study is in the direction of building up the most efficient Deep Learning-based analytical method for detecting electrical with mechanical errors in induction motors. On a machine fault simulator, electrical with mechanical errors of various harshness levels are investigated by obtaining vibrational with current signals. Herein paper, an optimal Deep Neural Network model is constructed founded on crucial electrical with mechanical properties, and it is then utilized to determine the type of defects that the given IM is dealing with in a timely and effective manner. In the result and discussion section, the findings are combined and discussed.