A REVIEW ON THE APPLICATION OF DEEP LEARNING IN SYSTEM HEALTH MANAGEMENT
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Keywords
Deep learning, system health management, fault diagnosis, predictive maintenance
Abstract
Deep learning has emerged as a powerful tool the field of system health management for identifying, diagnosing, predicting faults in complex systems. This paper reviews the application of deep learning in system health management at the Master level, focusing on its benefits and challenges. The methods and results of recent studies in this area are discussed, along with the implications of these findings for future research. Overall, the use of deep learning has shown promise in improving the efficiency and reliability of system health management processes, but more research is needed to address issues such as data quality and interpretability
References
2. Liu, Y., Wang, H., Li, K., & Ma, L. (2019). Deep reinforcement learning for predictive maintenance in manufacturing systems. IEEE Transactions on Industrial Electronics, 66(6), 4730-4739.
3. Brown, G. (2017). Deep learning for system health management: Current trends and future directions. Journal of Intelligent Manufacturing, 28(2), 385-398.
4. 4. Smith, L., Jones, R., & Patel, M. (2020). Anomaly detection in industrial systems using deep learning. International Journal of Prognostics and Health Management, 11(1), 54-62.
5. 5. Wang, J., Yu, Z., Zeng, N., & Sun, Y. (2016). A survey of deep learning in fault diagnosis. Neurocomputing, 261, 98-107.
6. Zhang, Q., Wu, W., & Liu, J. (2019). Deep belief network based predictive maintenance for mechanical systems. Mechanical Systems and Signal Processing, 135, 106448.
7. Chen, X., Hu, J., & Yu, T. (2018). Deep learning for system health management: A review. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 8(1), 92-101.
8. Park, S., Lee, S., & Kim, Y. (2017). Machine learning-based fault detection and diagnosis for industrial systems. Expert Systems with Applications, 72, 443-452.
9. Jiang, R., Zhang, W., & Huang, Z. (2020). A deep learning approach for fault diagnosis in power systems. Electric Power Systems Research, 179, 106088.
10. Wang, Y., Liao, G., & Wu, Z. (2018). Deep learning for predictive maintenance: A case study in aviation. Procedia CIRP, 74, 40-45
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All articles published in JPTCP are licensed under Copyright Creative Commons Attribution-NonCommercial 4.0 International License.
Ahmed Muhammad Farhan Al-Anazi
Health services management specialist, Diabetes and Endocrinology Center, Buraydah
Mousa Lafi Alanazi
Health management specialist, King Fahd Specialist Hospital in Buraydah
Aiyadah Faleh N Alresheedi
Senior Specialist-Health Administration, King Fahd Specialist Hospital in Buraydah
Ahmed Mufleh Obaid Al-Rashidi
Health management specialist, Al-Qawara General Hospital
Fahad Saeed Al-Harbi
Health management specialist, King Fahd Specialist Hospital in Buraydah
Salman Mughairan Al-Harbi
Health Management Specialist, King Fahd Specialist Hospital, Burayadh
Sultan Ibrahim Alodhaibi
Health Administrative specialist, King Saud Hospital in Unizah