Health Monitoring of Induction Machines - A Review

The increasing demand for reliability and efficiency in industrial applications necessitates advanced health monitoring systems for induction machines. Traditional diagnostic methods often fail to handle complex machine dynamics and diverse fault conditions effectively. Computational intelligence (CI) techniques, including artificial neural networks, evolutionary algorithms, fuzzy logic, and deep learning, offer robust solutions for fault detection, diagnosis, and predictive maintenance. This paper reviews the role of CI-based approaches in analyzing motor performance, identifying faults such as rotor bar defects, bearing failures, and stator winding issues, and predicting failures before they escalate. Furthermore, the integration of CI with IoT-enabled smart monitoring systems, enabling real-time data processing and decision-making can also be explored. The study highlights the advantages of these methods in enhancing fault classification accuracy, reducing maintenance costs, and improving system longevity. This paper suggests that CI-based health monitoring is a transformative step towards autonomous and intelligent condition-based maintenance of induction machines.