PREDICTIVE MAINTENANCE IN MANUFACTURING: AI AND ML APPLICATIONS

Abstract

Predictive maintenance (PdM) in manufacturing has gained significant attention with the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies. PdM aims to predict the failure of equipment and machinery before it occurs, thereby reducing downtime, improving asset utilization, and optimizing maintenance costs. AI and ML algorithms are utilized to analyze large volumes of data from sensors, historical maintenance records, and operational conditions. These advanced technologies can detect patterns and anomalies that may indicate impending failures, enabling manufacturers to schedule maintenance activities at optimal times, rather than relying on reactive or time-based strategies. This paper explores various AI and ML techniques applied in predictive maintenance, including supervised and unsupervised learning, deep learning, and reinforcement learning, and their impact on manufacturing efficiency. The integration of these technologies results in enhanced decision-making, real-time monitoring, and an overall improvement in the operational lifecycle of manufacturing equipment. The challenges, including data quality, system integration, and scalability, are also discussed, alongside the future directions for AI and ML in predictive maintenance.