Abstract:

Production industry equipment failures can contribute to high downtime, high maintenance expenses, and safety hazards. Reactive maintenance and preventive maintenance are conventional methods that tend to be unsuccessful in achieving maximum reliability and cost-effectiveness. Predictive maintenance, fueled by machine learning (ML), provides a proactive strategy by examining sensor information and operation parameters to predict failures before they actually happen. Yet, difficulties including data quality issues, interpretability of models, and real-time deployment discourage its universal application. This paper examines the application of ML methods, such as supervised learning, anomaly detection, and deep learning, in predictive maintenance. A step-by-step approach to data gathering, preprocessing, model selection, and validation is discussed, focusing on performance indicators like accuracy, precision-recall, and F1-score. Deployment approaches such as cloud and edge computing integration are also outlined. The findings bring to the forefront the efficacy of ML models to enhance equipment reliability and lower operation costs. Cost-benefit analysis illustrates the resultant savings through predictive maintenance. Challenges such as data bias and implementation challenges in the real world are also identified in the study. Recommendations for industrial implementation and avenues for future work, such as model interpretability and real-time AI-based automation, are suggested to improve scalability and usability of predictive maintenance applications.