AI-Powered Predictive Maintenance in Manufacturing: Enhancing Efficiency and Reducing Downtime
Abstract
Predictive maintenance (PdM) powered by artificial intelligence (AI) has emerged as a transformative approach to enhancing operational efficiency and reducing downtime in manufacturing. This study explores the integration of AI techniques, including machine learning and data analytics, to predict equipment failures and optimize maintenance schedules. By analyzing historical and real-time data from manufacturing equipment, the AI-driven PdM system identifies patterns and anomalies that precede failures. The implementation of this system across multiple manufacturing sites demonstrated a significant reduction in unplanned downtime and maintenance costs, underscoring the potential of AI in revolutionizing industrial maintenance practices.
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References
- Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.
- Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780.
- Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. OTexts.
- Liaw, A., & Wiener, M. (2002). Classification and Regression by randomForest. R News, 2(3), 18-22.
- Smola, A. J., & Schölkopf, B. (2004). A tutorial on Support Vector Regression. Statistics and Computing, 14(3), 199-222.
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