Knowledge formalization for experts’ selection into a collaborative maintenance platform

Abstract

Over the past twenty years, the maintenance function has undergone considerable change to respond to advanced technologies of information and communication (NTIC) with successive concepts, emaintenance and s-maintenance. The important role of the human factor in the problems related to conduct industrial processes is indisputable and is justified by a greater diversity of research. Indeed, the consideration of the human characteristics is essential to enhance the factory performance. Particularly, reuse of human knowledge is an element which plays an essential role in the continuous improvement of maintenance activities. In this context, we are interested to the problem of selection of experts in maintenance process. We propose, in this paper, a new approach to formalize knowledge as first step to solve the experts' selection problem under the previous experience. In that purpose, this paper takes domain ontology as a framework, the experience feedback models used to select experts, in the purpose to collaborate the interactions between them as a team that is capable of making decision in a fault diagnosis/repair situation based not only on skills but also according to the previous experience and CMDO ontology as the knowledge domain.

Theoretical B2B knowledge management framework focused on value co-creation

“…In addition, even when its value is considered for the best business performance, when knowledge is at risk, meaning knowledge waste, loss, leakage, spillover, hinder, there may prevent the usage of all the knowledge capability (Ferenhof, 2016). Within this scenario, the difficulty of sharing knowledge results in practical needs no longer be resolved in companies (Bekkaoui, 2015). In this sense, Krb alek and Vacek (2011) state that the lack of mapping and storage knowledge may interrupt access when needed.…”

An Approach to Supporting the Selection of Maintenance Experts in the Context of Industry 4.0

“…Acquired knowledge must be converted into extracted and explicit knowledge, so that it can be computerised. This can be done [29] by using the frames based systems [30], frame logic [31], semantic networks [32], and conceptual graphs, with these being methods based on concept dictionaries, viz., ontologies [29], and methods that are based on established rules. The rule-based method was selected in order to create a formalised base for the approach (Figure 1).…”