Review of Machine Learning Algorithms for Predictive Maintenance in Industrial Applications

Predictive maintenance (PdM) has gained increasing importance in modern industries due to its ability to anticipate equipment failures and extend asset life. By leveraging machine learning (ML) techniques, PdM systems can analyze large volumes of operational data, identify early signs of wear, and predict remaining useful life (RUL) more accurately than traditional methods. This review delves into the application of various ML algorithms in PdM, including artificial neural networks, support vector machines, clustering algorithms, and random forests. We discuss how these algorithms enhance the ability to detect anomalies, optimize maintenance schedules, and reduce unnecessary maintenance costs. Additionally, we evaluate the challenges associated with data quality, feature selection, and model interpretability in real-world PdM implementations. The paper also provides insights into future trends, such as the integration of deep learning and IoT technologies to enable real-time monitoring and dynamic maintenance strategies that can adapt to complex industrial environments. Overall, this review underscores the transformative potential of ML-driven predictive maintenance in achieving more efficient, cost-effective, and reliable industrial operations.