PREDICTIVE MACHINE MAINTENANCE USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Unexpected machine failures can interrupt production, increase repair expenses, and create safety risks. Predictive machine maintenance usessensor data, Artificial Intelligence (AI), and Machine Learning (ML) to estimate when equipment may develop a fault. Instead of servicing every machine atfixed intervals or waiting for a breakdown, the system continuously monitors parameters such as vibration, temperature, pressure, current, and operating hours.Data is cleaned and analysed, and a trained ML model identifies abnormal behaviour or predicts the remaining useful life of a component. The system can thengenerate an alert so that maintenance is planned before failure occurs. This paper explains the concept, working process, major technologies, model design,applications, benefits, limitations, and a practical implementation framework. It also discusses data quality, model evaluation, cybersecurity, and humanverification. Predictive maintenance can improve equipment availability and maintenance planning, but its results depend on representative data, suitable