Condition Monitoring & Predictive Maintenance in Electric Drives

Abstract

Electric drives are widely used in industrial, transportation, and domestic applications due to their efficiency, reliability, and precise control. However, operational stresses and environmental factors can degrade the performance of electric drives over time, leading to unexpected failures. Condition monitoring (CM) and predictive maintenance (PdM) have emerged as essential strategies to detect early signs of degradation, prevent unplanned downtime, and optimize maintenance schedules. This paper presents a comprehensive review of the principles, techniques, and applications of CM and PdM in electric drives. Various monitoring parameters such as vibration, temperature, current, and acoustic signals are discussed along with diagnostic methods including signal processing, artificial intelligence, and machine learning approaches. The role of predictive analytics in improving reliability, extending equipment lifespan, and reducing maintenance costs is highlighted. The paper also presents comparative studies of different CM techniques, case studies, and future trends in smart maintenance strategies.

KEYWORDS: Electric drives, Condition monitoring, Predictive maintenance, Fault diagnosis, Vibration analysis, Machine learning.