Intelligent Maintenance using Machine Learning has emerged as a key component of modern industrial systems by enabling predictive and condition-based maintenance strategies through data-driven decision-making. Machine Learning (ML), Artificial Intelligence (AI), Internet of Things (IoT), Big Data Analytics, Cloud Computing, Edge Computing, and Digital Twin technologies have significantly transformed traditional maintenance practices into intelligent maintenance systems capable of predicting equipment failures before they occur. These technologies continuously analyze sensor data, operational parameters, vibration signals, temperature variations, acoustic emissions, and historical maintenance records to identify anomalies and estimate the remaining useful life (RUL) of industrial equipment.
Traditional maintenance strategies, including corrective and preventive maintenance, often result in unexpected equipment failures, excessive maintenance costs, production downtime, and inefficient resource utilization. Intelligent maintenance overcomes these limitations by employing Machine Learning algorithms such as Decision Trees, Random Forests, Support Vector Machines (SVM), Artificial Neural Networks (ANN), Deep Learning, Reinforcement Learning, and Ensemble Learning for fault diagnosis, predictive maintenance, anomaly detection, and maintenance scheduling.
