Abstract:

Predictive maintenance (PdM) in industrial systems has emerged as a key strategy for minimizing downtime, reducing costs, and optimizing operational efficiency. Machine learning (ML) plays a pivotal role in enabling PdM by analyzing historical and real-time data to predict equipment failures and recommend timely maintenance actions. This paper provides a comprehensive overview of the use of ML techniques for predictive maintenance in industrial settings. It discusses various data sources, preprocessing methods, and feature extraction techniques critical for effective implementation. Key machine learning algorithms, including supervised, unsupervised, and deep learning approaches, are reviewed in the context of their application to PdM. The paper also highlights challenges such as data quality, scalability, and model interpretability, offering potential solutions and future research directions to address these issues. Case studies from different industries illustrate the practical applications and benefits of ML-driven PdM. Finally, the paper emphasizes the importance of integrating domain knowledge with ML techniques to achieve robust and reliable predictive maintenance solutions.

Description:

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