Acoustic-Based Detection of Stator Faults in Induction Motors

Detection of stator faults in electric motors is very Important. The non-invasive identification of stator problems in electric motors is the main goal of this research project, which focuses on inter-turn short circuits and insulation deterioration in phase windings. Using Acoustic signals produced during dynamic motor activity, we suggest a unique strategy that departs from conventional invasive methods that rely on current and voltage data. By examining these data, we hope to create advanced machine learning models and signal processing algorithms that can differentiate between ideal and flawed operating circumstances. Early stator failure diagnosis, reduced downtime, and improved motor reliability are all possible with this non-invasive technique. This novel method could greatly increase operational effectiveness and prolong the life of electric motors in a variety of industrial applications when incorporated into motor condition monitoring systems.