Machine Learning Approaches for Predictive Maintenance in IoT Devices

Predictive maintenance (PdM) has emerged as a crucial strategy in managing Internet of Things (IoT) devices. By anticipating failures and enabling timely repairs, predictive maintenance minimizes downtime, enhances operational efficiency, and reduces maintenance costs. With the rise of IoT, the amount of data generated by interconnected devices has escalated, presenting both an opportunity and a challenge in maintaining these systems. Machine learning (ML) techniques, including supervised learning, unsupervised learning, and reinforcement learning, have shown significant potential in harnessing the data from IoT devices to predict failures before they occur. This paper explores various machine learning approaches to predictive maintenance in IoT devices, including data preprocessing, feature extraction, and model training. We evaluate the performance of different machine learning algorithms such as decision trees, random forests, support vector machines (SVM), and deep learning models in terms of their accuracy, precision, and computational efficiency. Experimental results highlight the strengths and limitations of each approach. Moreover, we discuss the integration of these models within the IoT ecosystem to improve maintenance strategies. The paper concludes with insights on how machine learning can be further enhanced to provide more robust solutions for predictive maintenance in IoT devices.