Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.15239 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912493421461504 |
|---|---|
| author | Wang, Qianchao Ding, Yuxuan Jia, Chuanzhen Li, Zhe Du, Yaping |
| author_facet | Wang, Qianchao Ding, Yuxuan Jia, Chuanzhen Li, Zhe Du, Yaping |
| contents | Novel AI-based arc fault diagnosis models have demonstrated outstanding performance in terms of classification accuracy. However, an inherent problem is whether these models can actually be trusted to find arc faults. In this light, this work proposes a soft evaluation indicator that explains the outputs of arc fault diagnosis models, by defining the the correct explanation of arc faults and leveraging Explainable Artificial Intelligence and real arc fault experiments. Meanwhile, a lightweight balanced neural network is proposed to guarantee competitive accuracy and soft feature extraction score. In our experiments, several traditional machine learning methods and deep learning methods across two arc fault datasets with different sample times and noise levels are utilized to test the effectiveness of the soft evaluation indicator. Through this approach, the arc fault diagnosis models are easy to understand and trust, allowing practitioners to make informed and trustworthy decisions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15239 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Explainable Artificial Intelligence based Soft Evaluation Indicator for Arc Fault Diagnosis Wang, Qianchao Ding, Yuxuan Jia, Chuanzhen Li, Zhe Du, Yaping Artificial Intelligence Signal Processing Novel AI-based arc fault diagnosis models have demonstrated outstanding performance in terms of classification accuracy. However, an inherent problem is whether these models can actually be trusted to find arc faults. In this light, this work proposes a soft evaluation indicator that explains the outputs of arc fault diagnosis models, by defining the the correct explanation of arc faults and leveraging Explainable Artificial Intelligence and real arc fault experiments. Meanwhile, a lightweight balanced neural network is proposed to guarantee competitive accuracy and soft feature extraction score. In our experiments, several traditional machine learning methods and deep learning methods across two arc fault datasets with different sample times and noise levels are utilized to test the effectiveness of the soft evaluation indicator. Through this approach, the arc fault diagnosis models are easy to understand and trust, allowing practitioners to make informed and trustworthy decisions. |
| title | Explainable Artificial Intelligence based Soft Evaluation Indicator for Arc Fault Diagnosis |
| topic | Artificial Intelligence Signal Processing |
| url | https://arxiv.org/abs/2507.15239 |