BRIEF RESEARCH REPORT article
Front. Energy Res.
Sec. Electrochemical Energy Storage
Volume 13 - 2025 | doi: 10.3389/fenrg.2025.1647197
This article is part of the Research TopicAdvances in Battery TechnologiesView all 3 articles
Robust Fault Detection in Electrochemical Energy Storage Systems Under Label Noise: Applications to Lithium-Ion Batteries and Transformer Windings
Provisionally accepted
- State Grid Anhui Electric Power Co., Ltd., Ma'anshan Power Supply Company, Ma'anshan, Anhui, China
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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.
Keywords: Fault diagnosis, Robust classification, Kernel Density Estimation, Label noise, lithium-ion batteries, transformer windings
Received: 15 Jun 2025; Accepted: 16 Jul 2025.
Copyright: © 2025 He, Liu, Wu and Wei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Tao He, State Grid Anhui Electric Power Co., Ltd., Ma'anshan Power Supply Company, Ma'anshan, Anhui, China
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