Distributed, Collaborative Awareness Model for Maintenance Decision Support.

Document identifier: oai:DiVA.org:ltu-77558
Keyword: Engineering and Technology, Mechanical Engineering, Production Engineering, Human Work Science and Ergonomics, Teknik och teknologier, Maskinteknik, Produktionsteknik, arbetsvetenskap och ergonomi, Industry 4.0, Maintenance, Decision support, Situation awareness, Collaboration, Augmented Reality, Drift och underhållsteknik, Operation and Maintenance
Publication year: 2020
Relevant Sustainable Development Goals (SDGs):
SDG 9 Industry, innovation and infrastructure
The SDG label(s) above have been assigned by OSDG.ai


Maintenance decision errors can result in very costly problems. With the rise of 4th industrial revolution, Intelligent Decision Support Systems are growing quickly. However, a key concern has been to better understand the linkage between the technicians’ knowledge and 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. As the foundation of the new model, is the assentation of 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 identification of two main gaps: first, there is no enough recognition of the technicians’ knowledge; second, there is little assistance for technician by providing the bigger picture. We used cognitive fit theory, and the theory of distributed situation awareness to build the new theoretical model we call the “Distributed Collaborative Awareness Model.” The model considers both explicit and implicit knowledge, and accommodates the dynamic challenges involved in operational level maintenance. As a means of applying this model, we identify and recommend some technological developments required in Augmented Reality based maintenance decision support.


Prasanna Illankoon

Luleå tekniska universitet; Drift, underhåll och akustik; Human Factors
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Phillip Tretten

Luleå tekniska universitet; Drift, underhåll och akustik
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