LEIPL: A novel fault diagnosis method for high-speed train axle box bearings under noisy labels.

Authors

Jiacheng Liang, Kai Zhang, Zhihao Guo, Yonghao Xia, Yinjie Guan, Qing Zheng, Chunhua Zhao

Published in

ISA transactions. Aug 06, 2026. Epub Aug 06, 2026.

Abstract

Reliable fault diagnosis of high-speed-train axle box bearings is challenged by noisy annotations in maintenance data. This paper proposes LEIPL, a noise-robust diagnostic method that expands the original label space with an auxiliary negative class and unifies positive and negative supervision. The original noisy label and top-K predictions are used to construct candidate positive labels, while the remaining labels provide negative supervision. A confidence-regularization term is further introduced to stabilize model optimization. Experiments on the HTBF and BJTU-RAO datasets show that LEIPL achieves accuracies of 99.16% and 99.20%, respectively, under -4 dB Gaussian white noise and 50% symmetric label noise. The results demonstrate strong robustness and low computational complexity under severe noisy-label conditions.

PMID:
42567764
Bibliographic data and abstract were imported from PubMed on 08 Aug 2026.

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