Classification and Fault Detection in Induction Motors Using DDACMD for Electrical Signal Analysis

The article examines the application of the DDACMD technique for analyzing electrical current signals from induction motors to classify their operational conditions. Specifically, the study focuses on categorizing the motor into three distinct states: healthy motor, motor with one broken bar, and motor with two broken bars. To achieve this, the research involves collecting electrical current signals from the motor under various operating conditions. These signals are then processed using DDACMD, a technique designed to extract and analyze distinctive features related to each condition. The processed data are evaluated using classification algorithms that interpret these features to accurately determine the motor's condition. The results of the analysis demonstrate that DDACMD is highly effective in distinguishing between the different motor conditions with a high level of accuracy. This effectiveness highlights the technique's potential for supporting predictive maintenance strategies, allowing for early detection of faults and thereby reducing costs associated with unexpected motor downtimes. The study concludes that DDACMD provides a reliable and precise means of diagnosing faults in induction motors. Its ability to accurately classify motor conditions makes it a valuable tool for enhancing maintenance practices. The article also suggests that further research should explore the broader applications of DDACMD in various fault detection scenarios and different types of machinery to fully leverage its diagnostic capabilities. This could significantly improve preventive maintenance efforts and operational efficiency across diverse industrial settings.