Recent Trends in Electrical Machines and Drives ISSN: 3139-2067 (Online)

ABSTRACT

Machine condition monitoring and predictive maintenance have evolved from scheduled inspections to data-driven strategies that leverage machine learning (ML) to anticipate faults, reduce downtime, and optimize maintenance costs. This paper surveys the role of machine learning in condition monitoring and predictive maintenance with focus on algorithms, feature extraction, data sources, implementation challenges, and performance evaluation. Real-world case studies are complemented with simulation examples, comparative tables, and figures illustrating workflows and model performance.

KEYWORDS: Machine learning, condition monitoring, predictive maintenance, anomaly detection, time-series features, classification, regression.