Abstract
Data-driven prognostics and health management (PHM) is key to increasing the productivity of industrial processes through accurate maintenance planning. The increasing complexity of the systems themselves, in addition to cyber-physical connectivity, has brought too many challenges for the discipline. As a result, data complexity challenges have been pushed back to include more decentralized learning challenges. In this context, this perspective paper describes these challenges and provides future directions based on a relevant state-of-the-art review.
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Reference25 articles.
1. Berghout, T., Mouss, M.-D., Mouss, L., and Benbouzid, M. (2022). ProgNet: A Transferable Deep Network for Aircraft Engine Damage Propagation Prognosis under Real Flight Conditions. Aerospace, 10.
2. Machinery health prognostics: A systematic review from data acquisition to RUL prediction;Lei;Mech. Syst. Signal Process.,2018
3. Berghout, T., and Benbouzid, M. (2022). A Systematic Guide for Predicting Remaining Useful Life with Machine Learning. Electronics, 11.
4. Machine Learning for Cybersecurity in Smart Grids: A Comprehensive Review-based Study on Methods, Solutions, and Prospects;Berghout;Int. J. Crit. Infrastruct. Prot.,2022
5. Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Chebel-Morello, B., Zerhouni, N., and Varnier, C. (2012, January 18–21). PRONOSTIA: An experimental platform for bearings accelerated degradation tests. Proceedings of the IEEE International Conference on Prognostics and Health Management, PHM’12, Denver, CO, USA.
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献