An estimating methodology for the load of train axle box bearings

Abstract

Axle box bearings serve as crucial components within the transmission system of high-speed trains. Their failure can directly impact the operational safety of these trains. Accurately determining the dynamic load experienced by bearings during the operation of high-speed trains can provide valuable boundary inputs for the study of bearing fatigue life and service performance, thereby holding significant engineering implications. In this study, we propose a high-speed train axle box bearing load estimation method (FMCC-DKF). This method is founded on the Kalman filtering technique of the Maximum Correntropy Criterion (MCC) and employs dummy measurement technology to enhance the stability of estimated loads. We develop a kernel size update algorithm to address the challenges associated with obtaining the key parameter, kernel size of MCC. Comparative analysis of the vertical and lateral loads of the axle box bearing obtained using FMCC-DKF, DKF, and AMCC-DKF, under both measurement noise-free and non-Gaussian noise conditions, is conducted to demonstrate the superiority of the proposed estimation method. The results indicate that the proposed FMCC-DKF method exhibits high estimation accuracy under both measurement noise-free and non-Gaussian noise interference, and maintains its high estimation accuracy despite changes in train speed. The proposed load estimation method demonstrates reliable performance within the low-frequency domain below 70 Hz.

Keywords

Axle box bearing load / Load estimation / Maximum correntropy criterion / Non-Gaussian noise / High-speed train

CRediT authorship contribution statement

Yabo Zhou: Data curation. Wubin Cai: Data curation. Maoru Chi: Funding acquisition. Zhenqian Li: Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

National Key R&D Program of China (Grant numbers 2022YFB4301201-11, 2022YFB4301203-05). National Natural Science Foundation of China (Grant number 52202464).

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