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Mrs. N. Lavanya is an Assistant Professor in the Department of Information Technology at Narasimha Reddy Engineering College, Hyderabad, Telangana. Her contribution focuses on machine learning and predictive maintenance applications in industrial systems.
Predictive maintenance has become an essential use of machine learning in contemporary industrial systems. Predictive maintenance, in contrast to conventional reactive and preventative measures, employs both real-time and historical operational data to discern equipment deterioration, identify abnormalities, forecast possible failures, and assess the remaining usable life of industrial assets. This section elucidates the essential principles, techniques, and uses of machine learning for anticipatory maintenance in industrial settings. It examines the collection of industrial data via sensors and Industrial Internet of Things (IIoT) frameworks, data preprocessing, feature engineering, machine learning methodologies, deep learning strategies, anomaly identification, defect diagnosis, and predictions of remaining useful life. The section moreover examines the amalgamation of edge computing, digital twins, explicable artificial intelligence, and federated learning to create scalable and intelligent upkeep frameworks. A cohesive machine learning framework for predictive maintenance is introduced, linking data collection, condition assessment, predictive modelling, failure evaluation, and maintenance planning. Ultimately, significant obstacles and prospective research avenues concerning data integrity, model elucidation, computational intricacy, cybersecurity, and instantaneous implementation are examined.