@article{wellsandt_towards_2021,
	series = {17th {IFAC} {Symposium} on {Information} {Control} {Problems} in {Manufacturing} {INCOM} 2021},
	title = {Towards {Using} {Digital} {Intelligent} {Assistants} to {Put} {Humans} in the {Loop} of {Predictive} {Maintenance} {Systems}},
	volume = {54},
	issn = {2405-8963},
	url = {https://www.sciencedirect.com/science/article/pii/S2405896321007047},
	doi = {10.1016/j.ifacol.2021.08.005},
	abstract = {Predictive maintenance systems are socio-technical systems where the interaction between maintenance personnel and the technical system is critical to achieving maintenance goals. Employees who use a predictive maintenance system should explore, modify, and verify their analysis and decision-making methods and rules. Conventional modes of interaction make this difficult since they are often hard to understand, obtrusive and unintuitive. Digital Intelligent Assistants (DIAs) provide fast, intuitive, and potentially hands-free access to systems through voice-based interaction and cognitive assistance. This paper introduces a novel approach to interact with predictive maintenance systems through DIAs. The aim is to integrate human knowledge more effectively into the predictive maintenance process to create a hybrid-intelligence system. In such systems, humans and computers complement and evolve together.},
	language = {en},
	number = {1},
	urldate = {2021-11-15},
	journal = {IFAC-PapersOnLine},
	author = {Wellsandt, Stefan and Klein, Konstantin and Hribernik, Karl and Lewandowski, Marco and Bousdekis, Alexandros and Mentzas, Gregoris and Thoben, Klaus-Dieter},
	month = jan,
	year = {2021},
	keywords = {Engineering Applications of Artificial Intelligence, Human-Automation Integration, Hybrid Intelligence Systems, Predictive Maintenance},
	pages = {49--54},
}

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