Explainable Predictive Maintenance. Pashami, S., Nowaczyk, S., Fan, Y., Jakubowski, J., Paiva, N., Davari, N., Bobek, S., Jamshidi, S., Sarmadi, H., Alabdallah, A., Ribeiro, R. P., Veloso, B., Sayed-Mouchaweh, M., Rajaoarisoa, L., Nalepa, G. J., & Gama, J. June, 2023. arXiv:2306.05120 [cs]
Explainable Predictive Maintenance [link]Paper  doi  abstract   bibtex   
Explainable Artificial Intelligence (XAI) fills the role of a critical interface fostering interactions between sophisticated intelligent systems and diverse individuals, including data scientists, domain experts, end-users, and more. It aids in deciphering the intricate internal mechanisms of ``black box'' Machine Learning (ML), rendering the reasons behind their decisions more understandable. However, current research in XAI primarily focuses on two aspects; ways to facilitate user trust, or to debug and refine the ML model. The majority of it falls short of recognising the diverse types of explanations needed in broader contexts, as different users and varied application areas necessitate solutions tailored to their specific needs. One such domain is Predictive Maintenance (PdM), an exploding area of research under the Industry 4.0 \& 5.0 umbrella. This position paper highlights the gap between existing XAI methodologies and the specific requirements for explanations within industrial applications, particularly the Predictive Maintenance field. Despite explainability's crucial role, this subject remains a relatively under-explored area, making this paper a pioneering attempt to bring relevant challenges to the research community's attention. We provide an overview of predictive maintenance tasks and accentuate the need and varying purposes for corresponding explanations. We then list and describe XAI techniques commonly employed in the literature, discussing their suitability for PdM tasks. Finally, to make the ideas and claims more concrete, we demonstrate XAI applied in four specific industrial use cases: commercial vehicles, metro trains, steel plants, and wind farms, spotlighting areas requiring further research.
@misc{pashami_explainable_2023,
	title = {Explainable {Predictive} {Maintenance}},
	url = {http://arxiv.org/abs/2306.05120},
	doi = {10.48550/arXiv.2306.05120},
	abstract = {Explainable Artificial Intelligence (XAI) fills the role of a critical interface fostering interactions between sophisticated intelligent systems and diverse individuals, including data scientists, domain experts, end-users, and more. It aids in deciphering the intricate internal mechanisms of ``black box'' Machine Learning (ML), rendering the reasons behind their decisions more understandable. However, current research in XAI primarily focuses on two aspects; ways to facilitate user trust, or to debug and refine the ML model. The majority of it falls short of recognising the diverse types of explanations needed in broader contexts, as different users and varied application areas necessitate solutions tailored to their specific needs. One such domain is Predictive Maintenance (PdM), an exploding area of research under the Industry 4.0 {\textbackslash}\& 5.0 umbrella. This position paper highlights the gap between existing XAI methodologies and the specific requirements for explanations within industrial applications, particularly the Predictive Maintenance field. Despite explainability's crucial role, this subject remains a relatively under-explored area, making this paper a pioneering attempt to bring relevant challenges to the research community's attention. We provide an overview of predictive maintenance tasks and accentuate the need and varying purposes for corresponding explanations. We then list and describe XAI techniques commonly employed in the literature, discussing their suitability for PdM tasks. Finally, to make the ideas and claims more concrete, we demonstrate XAI applied in four specific industrial use cases: commercial vehicles, metro trains, steel plants, and wind farms, spotlighting areas requiring further research.},
	urldate = {2023-08-30},
	publisher = {arXiv},
	author = {Pashami, Sepideh and Nowaczyk, Slawomir and Fan, Yuantao and Jakubowski, Jakub and Paiva, Nuno and Davari, Narjes and Bobek, Szymon and Jamshidi, Samaneh and Sarmadi, Hamid and Alabdallah, Abdallah and Ribeiro, Rita P. and Veloso, Bruno and Sayed-Mouchaweh, Moamar and Rajaoarisoa, Lala and Nalepa, Grzegorz J. and Gama, João},
	month = jun,
	year = {2023},
	note = {arXiv:2306.05120 [cs]},
	keywords = {Computer Science - Artificial Intelligence, I.2.1},
}
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