Machine Learning Algorithms for Predictive Maintenance in Industrial IoT
Keywords:
Predictive Maintenance, Industrial IoT, Machine Learning, Support Vector Machines (SVM), Decision Trees, Random Forests.Abstract
Predictive maintenance is an essential strategy in industrial Internet of Things (IoT) environments, where the integration of machine learning algorithms can significantly enhance operational efficiency and reduce downtime. This paper explores various machine learning techniques employed for predictive maintenance, including supervised, unsupervised, and reinforcement learning models. We present a comprehensive analysis of algorithms such as support vector machines (SVM), decision trees, random forests, and deep learning approaches, evaluating their effectiveness in predicting equipment failures and optimizing maintenance schedules. Our findings reveal that machine learning-based predictive maintenance not only improves equipment reliability but also leads to substantial cost savings and operational improvements. Furthermore, we discuss the challenges of data acquisition, feature selection, and model interpretability in real-world industrial settings. This paper concludes with recommendations for future research directions and the practical implications of implementing machine learning in predictive maintenance frameworks within the industrial IoT landscape.