@inproceedings{8d1f77eac43a40bda99c3e29f91e0dde,
title = "Battery Abnormal Diagnosis of Electric Loader Based on Data Mining",
abstract = "In order to address battery degradation and inconsistency in electric loaders, this study proposes a data-driven fault diagnosis method based on cloud platform data. The approach combines t-SNE for dimensionality reduction, K-means for clustering, and a 3σ-MSS multi-level screening strategy to identify abnormal battery cells. Short-term and long-term deviation analyses were conducted. Results show that the proportion of deviated voltage is below 6\%, which can be mitigated via balancing charging. The method supports early fault warning and maintenance intervention.",
keywords = "anomaly detection, electric loader, fault diagnosis, power battery",
author = "Qiang Zhang and Muxin He and Ni Lin",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 8th IEEE International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2025 ; Conference date: 28-11-2025 Through 30-11-2025",
year = "2025",
doi = "10.1109/AUTEEE67053.2025.11322070",
language = "English",
series = "2025 IEEE 8th International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "150--153",
booktitle = "2025 IEEE 8th International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2025",
address = "United States",
}