Robust fault detection in electrochemical energy storage systems under label noise: applications to lithium-ion batteries and transformer windings
Reliable fault detection is essential for ensuring the safe and efficient operation of electrochemical energy storage systems, including lithium-ion batteries and transformer. However, the performance of machine learning-based fault diagnosis models is often degraded in practice due to label noise i...
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| Format: | Article |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2025.1647197/full |
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| author | Tao He Wei Liu Xin Wu Yu Wei |
| author_facet | Tao He Wei Liu Xin Wu Yu Wei |
| author_sort | Tao He |
| collection | DOAJ |
| description | Reliable fault detection is essential for ensuring the safe and efficient operation of electrochemical energy storage systems, including lithium-ion batteries and transformer. However, the performance of machine learning-based fault diagnosis models is often degraded in practice due to label noise in training data, caused by sensor inaccuracies, ambiguous fault transitions, and imperfect labeling processes. This paper proposes a lightweight and effective kernel-based data rectification framework to improve the robustness of fault detection under noisy label conditions. The method identifies and discards low-density data points that are statistically more likely to be mislabeled, using kernel density estimation and a tunable data discarding strategy. The approach is computationally efficient, classifier-agnostic, and easily applicable to existing fault diagnosis pipelines. We evaluate the proposed method on two datasets: simulated lithium-ion battery voltage data under various fault scenarios, and transformer winding oscillation wave data under multiple winding fault conditions. The results demonstrate that the rectification framework significantly improves classification accuracy across both Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers. Furthermore, the choice of discarding ratio is shown to be critical, with optimal performance achieved when the ratio is tuned close to the underlying noise level. These results highlight the potential of the proposed method to enhance the reliability of fault diagnosis in electrochemical energy storage systems. Future work will explore adaptive strategies to automatically optimize the rectification strength without requiring prior knowledge of the noise rate, and extend the framework to multi-sensor and multi-modal monitoring scenarios. |
| format | Article |
| id | doaj-art-8ea161e58fb64b7c9fb28cf4cf0bde94 |
| institution | Kabale University |
| issn | 2296-598X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj-art-8ea161e58fb64b7c9fb28cf4cf0bde942025-08-22T14:52:31ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2025-08-011310.3389/fenrg.2025.16471971647197Robust fault detection in electrochemical energy storage systems under label noise: applications to lithium-ion batteries and transformer windingsTao He0Wei Liu1Xin Wu2Yu Wei3State Grid Anhui Electric Power Co., Ltd., Ma’anshan Power Supply Company, Anhui, ChinaState Grid Anhui Electric Power Research Institute, Anhui, ChinaState Grid Anhui Electric Power Co., Ltd., Ma’anshan Power Supply Company, Anhui, ChinaState Grid Anhui Electric Power Co., Ltd., Ma’anshan Power Supply Company, Anhui, ChinaReliable fault detection is essential for ensuring the safe and efficient operation of electrochemical energy storage systems, including lithium-ion batteries and transformer. However, the performance of machine learning-based fault diagnosis models is often degraded in practice due to label noise in training data, caused by sensor inaccuracies, ambiguous fault transitions, and imperfect labeling processes. This paper proposes a lightweight and effective kernel-based data rectification framework to improve the robustness of fault detection under noisy label conditions. The method identifies and discards low-density data points that are statistically more likely to be mislabeled, using kernel density estimation and a tunable data discarding strategy. The approach is computationally efficient, classifier-agnostic, and easily applicable to existing fault diagnosis pipelines. We evaluate the proposed method on two datasets: simulated lithium-ion battery voltage data under various fault scenarios, and transformer winding oscillation wave data under multiple winding fault conditions. The results demonstrate that the rectification framework significantly improves classification accuracy across both Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers. Furthermore, the choice of discarding ratio is shown to be critical, with optimal performance achieved when the ratio is tuned close to the underlying noise level. These results highlight the potential of the proposed method to enhance the reliability of fault diagnosis in electrochemical energy storage systems. Future work will explore adaptive strategies to automatically optimize the rectification strength without requiring prior knowledge of the noise rate, and extend the framework to multi-sensor and multi-modal monitoring scenarios.https://www.frontiersin.org/articles/10.3389/fenrg.2025.1647197/fullfault diagnosisrobust classificationkernel density estimationlabel noiselithium-ion batteriestransformer windings |
| spellingShingle | Tao He Wei Liu Xin Wu Yu Wei Robust fault detection in electrochemical energy storage systems under label noise: applications to lithium-ion batteries and transformer windings Frontiers in Energy Research fault diagnosis robust classification kernel density estimation label noise lithium-ion batteries transformer windings |
| title | Robust fault detection in electrochemical energy storage systems under label noise: applications to lithium-ion batteries and transformer windings |
| title_full | Robust fault detection in electrochemical energy storage systems under label noise: applications to lithium-ion batteries and transformer windings |
| title_fullStr | Robust fault detection in electrochemical energy storage systems under label noise: applications to lithium-ion batteries and transformer windings |
| title_full_unstemmed | Robust fault detection in electrochemical energy storage systems under label noise: applications to lithium-ion batteries and transformer windings |
| title_short | Robust fault detection in electrochemical energy storage systems under label noise: applications to lithium-ion batteries and transformer windings |
| title_sort | robust fault detection in electrochemical energy storage systems under label noise applications to lithium ion batteries and transformer windings |
| topic | fault diagnosis robust classification kernel density estimation label noise lithium-ion batteries transformer windings |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2025.1647197/full |
| work_keys_str_mv | AT taohe robustfaultdetectioninelectrochemicalenergystoragesystemsunderlabelnoiseapplicationstolithiumionbatteriesandtransformerwindings AT weiliu robustfaultdetectioninelectrochemicalenergystoragesystemsunderlabelnoiseapplicationstolithiumionbatteriesandtransformerwindings AT xinwu robustfaultdetectioninelectrochemicalenergystoragesystemsunderlabelnoiseapplicationstolithiumionbatteriesandtransformerwindings AT yuwei robustfaultdetectioninelectrochemicalenergystoragesystemsunderlabelnoiseapplicationstolithiumionbatteriesandtransformerwindings |