Abstract
Predictive maintenance (PdM) has emerged as a key strategy for enhancing the reliability and operational efficiency of industrial machinery. With the increasing complexity of modern industrial systems, traditional maintenance methods are no longer sufficient to ensure optimum performance. Machine learning (ML) offers a powerful toolset to predict machine failures, enabling real-time monitoring and maintenance scheduling. This paper explores the various applications of ML techniques in predictive maintenance, focusing on its impact in reducing downtime, improving asset management, and lowering operational costs. Different ML models, such as regression analysis, decision trees, and neural networks, are evaluated for their effectiveness in forecasting maintenance needs. The paper concludes with an analysis of the challenges and future prospects of integrating ML in industrial maintenance practices.
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