Digital Twin : Visualization-Assisted Corrective Maintenance
Abstract: This thesis evaluates the significance of the Digital Twin based data-driven solution, in helping corrective maintenance technicians leverage their multi-disciplinary engineering skills to solve complex mechatronic problems. Due to the complex mechatronic nature of the faults, human involvement is necessary for corrective maintenance. Even today, many industries perform corrective maintenance by following methods that are both time inefficient and error prone. Software/AI based solutions have been widely reported to have failed due to neglect of human aspect in maintenance. The role of human cannot be completely replaced by software systems yet. Standard maintenance practices such as FMEA and RCA are costly, time consuming and susceptible to errors. On the other side, Digital Twin (DT) based solutions have shown to have improved management and effectiveness of maintenance by considering the human aspect. However, for corrective maintenance, the solution is still in its conceptual stage. There is a need to practically implement a Digital Twin based solution and quantitatively evaluate its significance. Recent studies have shown that Digital Twin concept, built on model-based approach, has a tremendous potential in providing all the essential data required to control the behaviour of a network of physical devices, and at the same time, virtually monitor their real-world states effectively. This thesis first attempts to develop user-centric visualizations built on a fully integrated digital twin of a complex Cyber Physical Production System (CPPS), and then it tries to evaluate its effectiveness (in terms of correctness and efficiency) in solving the corrective maintenance problem. Experimental results show that when the corrective maintenance task is assisted by user-centricvisualizations from a real-time Digital Twin, it significantly improved the accuracy and efficiency of the maintenance technician by about 24% and 52,4% respectively. Further, a post-experimental qualitative analysis explains that it is not any visualization but a Digital Twin based data-driven visualization, built on the user requirements that helped perform the corrective maintenance task more effectively.
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