Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.01360 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916466018746368 |
|---|---|
| author | Court, Killian Mc Court, Xavier Mc Du, Shijia Zeng, Zhiguo |
| author_facet | Court, Killian Mc Court, Xavier Mc Du, Shijia Zeng, Zhiguo |
| contents | Deep learning models have created great opportunities for data-driven fault diagnosis but they require large amount of labeled failure data for training. In this paper, we propose to use a digital twin to support developing data-driven fault diagnosis model to reduce the amount of failure data used in the training process. The developed fault diagnosis models are also able to diagnose component-level failures based on system-level condition-monitoring data. The proposed framework is evaluated on a real-world robot system. The results showed that the deep learning model trained by digital twins is able to diagnose the locations and modes of 9 faults/failure from $4$ different motors. However, the performance of the model trained by a digital twin can still be improved, especially when the digital twin model has some discrepancy with the real system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_01360 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Use Digital Twins to Support Fault Diagnosis From System-level Condition-monitoring Data Court, Killian Mc Court, Xavier Mc Du, Shijia Zeng, Zhiguo Machine Learning Robotics Deep learning models have created great opportunities for data-driven fault diagnosis but they require large amount of labeled failure data for training. In this paper, we propose to use a digital twin to support developing data-driven fault diagnosis model to reduce the amount of failure data used in the training process. The developed fault diagnosis models are also able to diagnose component-level failures based on system-level condition-monitoring data. The proposed framework is evaluated on a real-world robot system. The results showed that the deep learning model trained by digital twins is able to diagnose the locations and modes of 9 faults/failure from $4$ different motors. However, the performance of the model trained by a digital twin can still be improved, especially when the digital twin model has some discrepancy with the real system. |
| title | Use Digital Twins to Support Fault Diagnosis From System-level Condition-monitoring Data |
| topic | Machine Learning Robotics |
| url | https://arxiv.org/abs/2411.01360 |