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.
Keywords
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.
How to Cite
License
Copyright (c) 2024 Journal of Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
Similar Articles
- Hugo Tom, David Kim, Emily Parker, Advanced Threat Detection Using Machine Learning Algorithms , Journal of Technology: Vol. 2 No. 1 (2024): The Future of Renewable Energy Technologies
- Sarah Wilson, John Davis, AI-Driven Autonomous Vehicles: Enhancing Safety and Efficiency , Journal of Technology: Vol. 2 No. 2 (2024): Advances in Artificial Intelligence
- Maria Johnson, Alan Carter, AI in Healthcare: Revolutionizing Patient Diagnosis and Treatment , Journal of Technology: Vol. 2 No. 2 (2024): Advances in Artificial Intelligence
- David Brown, Lisa Green, AI-Driven Cybersecurity: Enhancing Threat Detection and Response , Journal of Technology: Vol. 2 No. 2 (2024): Advances in Artificial Intelligence
- Hugo Tom, Sarah Brown, James Miller, Integrating Robotics and the Internet of Things (IoT) for Smart Automation , Journal of Technology: Vol. 2 No. 2 (2024): Advances in Artificial Intelligence
- Robert Lee, Anna Smith, Explainable AI: Enhancing Transparency and Trust in Artificial Intelligence Systems , Journal of Technology: Vol. 2 No. 2 (2024): Advances in Artificial Intelligence
- Hugo Tom, Emily Thompson, Michael Lee, Enhancing Autonomous Robotic Navigation Through Reinforcement Learning , Journal of Technology: Vol. 2 No. 2 (2024): Advances in Artificial Intelligence
- Linda Robinson, Michael Brown, Hannah Lee, Augmented Reality (AR) in Education: Transforming Learning Experiences , Journal of Technology: Vol. 1 No. 1 (2024): Special Issue: Emerging Technologies in Artificial Intelligence
- John Smith, Laura Brown, Kevin White, Leveraging Internet of Things (IoT) for Smart City Infrastructure Management , Journal of Technology: Vol. 1 No. 1 (2024): Special Issue: Emerging Technologies in Artificial Intelligence
- Alice Johnson, Robert Lee, Maria Garcia, Enhancing Autonomous Vehicle Navigation Using Deep Reinforcement Learning , Journal of Technology: Vol. 1 No. 1 (2024): Special Issue: Emerging Technologies in Artificial Intelligence
You may also start an advanced similarity search for this article.
Issue Cover
Downloads
Statistics
- Abstract Views
- : 22 times
- : 1 times