Multi-spectrum Fusion Evaluation Method and Fault Diagnosis Method for High-speed Rail Bogie Bearings

doi:10.3901/JME.260038

Abstract

Abstract: The rail bogie is recognized as an important power transmission unit in high-speed trains, and the bogie bearing, serving as a load-bearing element within it, is subjected to prolonged alternating loads and is highly prone to failure. Existing frequency domain analysis methods for rolling bearing fault diagnosis are limited by strict applicability conditions. For example, the fast kurtogram method often fails when analyzing signals contaminated by interference or excessive background noise, significantly limiting the widespread practical application of those methods. To address these limitations, a fault diagnosis and decision-making method based on multi-spectrum fusion evaluation is developed. A weighted evaluation criterion is constructed for frequency domain analysis methods, considering three aspects-diagnostic effect, stability, and calculation time-to quantify their engineering suitability. Furthermore, a spectrum evaluation criterion is established to assess the diagnostic effectiveness of frequency domain analysis methods. To accommodate the distinct requirements of data accumulation and completion phases in high-speed rail operation, the proposed spectrum evaluation criterion incorporates downscaled spectral feature fusion and an optimized Fisher's discriminant ratio, respectively. This facilitates the selection of the fusion spectrum yielding optimal diagnostic performance. The validity of the method is confirmed through data collection experiments. The results demonstrate its good engineering applicability and robustness.

Key words: high-speed railway, bogie bearing, spectrum evaluation, frequency domain analysis method

CLC Number:

TP277

ZHANG Xingwu, SUN Haoyu, LI Yanqi, LIU Yilong, CHEN Xuefeng. Multi-spectrum Fusion Evaluation Method and Fault Diagnosis Method for High-speed Rail Bogie Bearings[J]. Journal of Mechanical Engineering, 2026, 62(2): 73-90.

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