Machine Learning Algorithms for Predictive Maintenance in Industrial Automation
Keywords:
Predictive Maintenance, Machine Learning, Industrial Automation, IoT SensorsAbstract
Predictive maintenance (PdM) is increasingly critical in modern industrial automation, reducing downtime, lowering costs, and improving system reliability. This paper reviews and evaluates various machine learning (ML) algorithms—such as Random Forest, Support Vector Machines (SVM), Neural Networks, and Gradient Boosting—for predictive maintenance in industrial contexts. By leveraging IoT-enabled sensor data, real-time monitoring, and historical system logs, these models can predict equipment failures before they occur. We present simulation results demonstrating failure prediction accuracy, remaining useful life (RUL) estimation, and decision latency for maintenance scheduling. The study also discusses challenges such as data imbalance, feature engineering, and model interpretability, and proposes a roadmap for integrating ML-based PdM into Industry 4.0 deployments.
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