Analysis of Machine Learning Algorithms for Predictive Maintenance in Industrial IOT Systems

Predictive Maintenance (PdM) is transforming the landscape of industrial operations by leveraging the capabilities of Industrial Internet of Things (IIoT) systems. By utilizing real-time data from interconnected sensors and devices, IIoT enables proactive detection and resolution of potential equipment failures, significantly reducing downtime and maintenance costs. Machine Learning (ML) algorithms play a critical role in predictive maintenance, providing accurate predictions, anomaly detection, and actionable insights by analyzing complex patterns in vast datasets. This paper evaluates the effectiveness of various ML algorithms for predictive maintenance in IIoT environments. It explores supervised, unsupervised, and reinforcement learning approaches, focusing on their performance, scalability, and practical implementation in industrial settings. The findings highlight the trade-offs between algorithm accuracy, interpretability, and computational efficiency, offering actionable recommendations for selecting suitable ML models for different industrial scenarios.