@article {7689,
	title = {Quo Vadis Machine Learning-Based Systems Condition Prognosis?\&mdash;A Perspective},
	journal = {Electronics },
	volume = {12},
	number = {3},
	year = {2023},
	pages = {527},
	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.

},
	url = {https://www.mdpi.com/2079-9292/12/3/527},
	author = {Mohamed Benbouzid and Berghout, Tarek}
}
