Fault Analysis of An Induction Motors Using AI and ML: An Efficient Way

The industry is becoming more and more focused on boosting the energy efficiency and lowering the operating expenses of induction motors. Frequent surveillance of the machine's status using monitoring systems that can discover breakdowns at an early stage may greatly minimize these expenses and enhance the motor's efficiency. Expensive shutdowns, unexpected downtime, breakdowns, and workplace accidents may be avoided by early defect identification. Motor losses and service life may be minimized by preventing and treating early signs of failure. Numerous review articles provide in-depth looks at certain defect detection methods for induction motors. All the methods discussed here can, however, only spot problems in their more advanced forms. We were unable to find any review that compares different methods for finding problems in their early phases of development. In this study, we examine existing methods and tools for early defect detection. The armature, rotor, and rolling bearings of a motor are the primary targets of this examination of current approaches. Methodologies are described, and suggestions for further study in this field are offered, for both steady-state and transient motor operation conditions.