Using Machine Learning and Deep Learning Algorithms for Downtime Minimization in Manufacturing Systems: An Early Failure Detection Diagnostic Service

Abstract

Accurate detection of possible machine failure allows manufacturers to identify potential fault situations in processes to avoid downtimes caused by unexpected tool wear or unacceptable workpiece quality. This paper aims to report the study of more than 20 fault detection models using Machine Learning (ML), Deep Learning (DL), and Deep Hybrid Learning (DHL). Predicting how the system could fail based on certain features or system settings (input variables) can help avoid future breakdowns and minimize downtime. The effectiveness of the proposed algorithms was experimented with a synthetic predictive maintenance dataset published by the School of Engineering of the University of Applied Sciences in Berlin, Germany. The fidelity of these algorithms was evaluated using performance measurement values such as accuracy, precision, recall, and the F-Score. Final results demonstrated that Deep Forest and Gradient Boosting algorithms had shown very high levels of average accuracy (exceeded 90%). Additionally, the Multinominal Logistic Regression and Long Short Term Memory based algorithms have shown satisfactory average accuracy (above 80%). Further analysis of models suggests that some models outperformed others. The research concluded that, through various ML, DL, and DHL algorithms, operational data analytics, and health monitoring system, engineers could optimize maintenance and reduce reliability risks.

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“…c) Maintenance Scheduling: By predicting when equipment is likely to fail, maintenance can be scheduled proactively to avoid unexpected downtime. This not only prevents costly disruptions in operations but also enables a more efficient use of resources [60]. d) Cost Savings: Predictive maintenance helps reduce maintenance costs by avoiding unnecessary preventive maintenance and reducing breakdowns that require reactive maintenance.…”

“…Furthermore, minimizing downtime improves productivity and efficiency [61]. e) Enhanced Equipment Lifespan: By maintaining equipment in optimal condition and preventing serious damage, predictive maintenance can extend the useful life of equipment [60].…”

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“…In today's globalized environment, industries constantly strive to re ne their processes [22,23]. Fluctuating customer expectations for high-quality products at reasonable prices and within short timeframes have compelled enterprises to adopt cutting-edge tools and state-of-the-art manufacturing systems.…”