Ontology-based Digital Twin for maintenance decisions in manufacturing systems: an application at laboratory scale

Abstract

In the Industry 4.0 environment, the maintenance of industrial assets is of increasing importance, and, in this domain, the recently emerged technology of Cognitive Digital Twin (CDT) is particularly suitable for the satisfaction of today’s manufacturers’ needs for flexibility, dynamism, broad vision of the systems, and responsiveness to stimuli. Although this technology shows considerable potential in supporting the execution of maintenance applications with minimum human intervention, in most cases the to-date technological level is not capable of achieving full automation, making the human role still fundamental in relation to the existing Digital Twin (DT) technologies. In this context, this paper proposes the development of an ontology-based DT aiming at supporting the maintenance fault diagnosis decision-making process in manufacturing systems through the synergistic exploitation of the maintenance ontology KARMA supported by algorithms and database technologies integrated with human knowledge. The solution is meant to enable the cognitive capabilities leading toward the CDT concept. The ontology-based Digital Twin has been applied and assessed in the reality-like facility TELMA, at the Research Center for Automatic Control (CRAN) in Nancy, through the implementation of a fault scenario.