Predictive Maintenance for Industrial Equipment Using Machine Learning

ABSTRACT Predictive maintenance for industrial equipment using machine learning involves leveraging advanced algorithms to predict equipment failures before they occur, minimizing downtime and reducing maintenance costs. By analyzing historical data from sensors, operational logs, and other sources, machine learning models can identify patterns and anomalies that indicate potential malfunctions. This approach helps in scheduling maintenance activities only when needed, improving operational efficiency, extending the lifespan of equipment, and enhancing overall productivity in industrial environments. This report focuses on the application of data-driven techniques to improve maintenance strategies in manufacturing and other industrial sectors. Traditional maintenance methods, such as reactive and preventive maintenance, often lead to unnecessary downtime or unexpected equipment failures. Machine learning, however, offers a more proactive approach by analyzing large volumes of real-time and historical data from sensors, machines, and operational systems to predict equipment failures. The process typically involves collecting data from various sources such as vibration sensors, temperature readings, pressure gauges, and machine logs. Machine learning algorithms, including regression models, decision trees, neural networks, and clustering techniques, are then applied to detect patterns, predict remaining useful life (RUL) of components, and identify early warning signs of failure. By continuously learning from new data, these models can adapt over time, improving the accuracy of predictions and enabling more precise maintenance scheduling.