Collaborating AI and human experts in the maintenance domain

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.

Keeping the organization in the loop: a socio-technical extension of human-centered artificial intelligence

“…Recent patents see maintenance workers and their activities as part of a neural maintenance network (Hishinuma and Osaki 2020). Other strategies focus more on models of collaboration between humans and AI in a maintenance system and emphasize the importance of both sets of knowledge (Illankoon and Tretten 2020). Some approaches focus more on the reduction of working hours and thus the proportion of human labor using more ML (Khalid et al 2020;Silva 2020).…”

How to Make Augmented Reality a Tool for Railway Maintenance Operations: Operator 4.0 Perspective

“…The distribution model of cognition has been adapted for this framework and focuses on developing an ensemble of distributed individuals and artifacts [33]. This model considers two indispensable parts: internal and external representations.…”

“…External representations are the knowledge structures coming from the environment. The environmental elements help to make sense of the dynamic working situation by providing information on physical symbols, objects, dimensions, constraints, and relations embedded in physical configurations [33]. Besides this, the environment provides information on what task is expected to be executed and who will participate in the procedure.…”

“…A cognitive and interactive tool can learn and improve with an operator acting as a mentor for the system, based on his/her experience and knowledge, whereas the system provides feedback to the human in return [35]. For the operator, the process of providing feedback and interactions ensures both increased efficiency and developed confidence in the system [33].…”

Augmented Reality reducing energy uses and COâ‚‚ emissions

“…Future features could be to develop learning methods with machine learning, image recognitions. By combining models [14] with the acquired recorded AR calls and images. Conditions are then shaped for AI applications which in turn could propose maintenance actions.…”