Collaborating AI and human experts in the maintenance domain
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Collaborating AI and human experts in the maintenance domain Prasanna Illankoon1 · Phillip Tretten1 Received: 13 March 2020 / Accepted: 14 September 2020 © The Author(s) 2020
Abstract Maintenance decision errors can result in very costly problems. The 4th industrial revolution has given new opportunities for the development of and use of intelligent decision support systems. With these technological advancements, key concerns focus on gaining a better understanding of the linkage between the technicians’ knowledge and the intelligent decision support systems. The research reported in this study has two primary objectives. (1) To propose a theoretical model that links technicians’ knowledge and intelligent decision support systems, and (2) to present a use case how to apply the theoretical model. The foundation of the new model builds upon two main streams of study in the decision support literature: “distribution” of knowledge among different agents, and “collaboration” of knowledge for reaching a shared goal. This study resulted in the identification of two main gaps: firstly, there must be a greater focus upon the technicians’ knowledge; secondly, technicians need assistance to maintain their focus on the big picture. We used the cognitive fit theory, and the theory of distributed situation awareness to propose the new theoretical model called “distributed collaborative awareness model.” The model considers both explicit and implicit knowledge and accommodates the dynamic challenges involved in operational level maintenance. As an application of this model, we identify and recommend some technological developments required in augmented reality based maintenance decision support. Keywords Industry 4.0 · Maintenance · Decision support · Situation awareness · Collaboration · Augmented reality
1 Introduction Erroneous maintenance decisions and their fatal consequences have been an ongoing concern. For example, maintenance decision error is a common denominator and appears in one form or another in nearly all aviation accidents (Kraus 2009; Marais and Robichaud 2012). The problem is that maintenance technicians are required to perform routine and non-routine complex tasks with different types of equipment, processes, and personnel (Raouf et al. 2006) under tight schedules often with little or no feedback (Liang et al. 2010) so they have difficulty developing adequate mental models about the consequences of their work (Endsley and Robertson 2000). Despite the emergence of many new technologies, the information flow at the technician level is mostly limited to conversation, job task cards, e-mails or whiteboards (Lall et al. 2017), and they have to deal with * Prasanna Illankoon [email protected] 1
poorly designed interfaces, and outdated and confusing manuals (Webel et al. 2013). These findings emphasize the importance of maintenance decision support (MDS). Maintenance actions can be seen as a combination of information sources, technologies, and physical tools, requiring maintenance data to
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