| Title: |
Architecting a Digital Twin-Based Predictive Maintenance System for Modelling Cable Joint Degradation |
| Authors: |
van Dinter, Raymon; Ekmekci, Görkem; Rieken, Sander; Tekinerdogan, Bedir; Catal, Cagatay |
| Source: |
4th Asia Pacific Conference of the Prognostics and Health Management |
| Publisher Information: |
The Prognostics and Health Management Society |
| Publication Year: |
2023 |
| Collection: |
Wageningen UR (University & Research Centre): Digital Library |
| Subject Terms: |
Life Science |
| Description: |
The large scale adoption of wind turbines and solar panels in the Netherlands places new demands on the medium voltage power grid. For example, highly varying loads can cause failures in certain cables. Cable joints are natural weak spots prone to faults due to varying currents, creating downtime challenges for public utility companies. Predictive maintenance (PdM) practices are necessary to minimize downtime for users. We present a Model-Based System Engineering approach using formal models and UML views to provide a scalable PdM design ontology for modeling cable joint degradation. We aim to monitor cable joint degradation from different manufacturers under varying conditions throughout the Netherlands in real-time using a Digital Twin (DT) approach. Our design provides high-resolution, real-time synchronization between the DT-based PdM system and the cable joints. The proposed architecture is scalable, robust, and flexible, and the software implementation is publicly available in an open-source repository. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
https://edepot.wur.nl/699200 |
| DOI: |
10.36001/phmap.2023.v4i1.3753 |
| Availability: |
https://research.wur.nl/en/publications/architecting-a-digital-twin-based-predictive-maintenance-system-f; https://doi.org/10.36001/phmap.2023.v4i1.3753; https://edepot.wur.nl/699200 |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ ; Wageningen University & Research |
| Accession Number: |
edsbas.69F51B24 |
| Database: |
BASE |