Threshold Adaptive Intelligent Health Monitoring Method for Axle Box Bearings of High-Speed Trains Under Multiple Operating Conditions

GAO Yang; RUAN Xinyi; YANG Bin; LEI Yaguo; LI Xiang; LI Naipeng

doi:10.7652/xjtuxb202507002

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Threshold Adaptive Intelligent Health Monitoring Method for Axle Box Bearings of High-Speed Trains Under Multiple Operating Conditions

Special Topic　Big Data Intelligent Fault Diagnosis and Prediction | 更新时间：2025-07-09

- Threshold Adaptive Intelligent Health Monitoring Method for Axle Box Bearings of High-Speed Trains Under Multiple Operating Conditions
- Journal of Xi’an Jiaotong University Vol. 59, Issue 7, Pages: 13-23(2025)
- 作者机构：
  
  1.西安交通大学现代设计及转子轴承系统教育部重点实验室， 710049，西安
  2.中车戚墅堰机车车辆工艺研究所股份有限公司， 213011，江苏常州
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202507002
  CLC： TH17
- Received：30 December 2024，
  
  Online First：01 April 2025，
  
  Published：10 July 2025
- 稿件说明：
移动端阅览
GAO Yang, RUAN Xinyi, YANG Bin, et al. Threshold Adaptive Intelligent Health Monitoring Method for Axle Box Bearings of High-Speed Trains Under Multiple Operating Conditions[J]. Journal of Xi’an Jiaotong University, 2025, 59(7): 13-23.
DOI：

GAO Yang, RUAN Xinyi, YANG Bin, et al. Threshold Adaptive Intelligent Health Monitoring Method for Axle Box Bearings of High-Speed Trains Under Multiple Operating Conditions[J]. Journal of Xi’an Jiaotong University, 2025, 59(7): 13-23. DOI： 10.7652/xjtuxb202507002.

摘要

为了克服高速列车轴箱轴承的健康监测指标随运营线路、速度变化而波动，解决传统阈值法难以基于统一标准进行轴承健康监测的问题，提出了多运营工况下高速列车轴箱轴承的阈值适配智能健康监测方法。首先，通过分析特征与转速的Spearman相关系数，对多运营速度下的轴承健康指标进行归一化处理，消除车速变化所产生的健康指标波动，从而实现监测阈值与列车运营工况的自动适配。然后，建立基于随机特征子集和主成分方向分割的改进孤立森林（SPCD-iForest）算法，利用多维特征提供的协同信息在数据的主成分方向对轴承的正常与故障状态进行划分，并在保持监测准确性的同时提高异常检测的计算效率。最后，通过列车轴箱轴承线路试验数据对提出的智能健康监测方法进行验证。结果表明：所提方法消除了列车运营工况变化对轴箱轴承健康指标的影响，其输出的异常因子为0~1之间的无量纲指标，可有效反映轴箱轴承健康状态的变化；相较于轨旁声学检测方法，能够提早10 d以上发现轴承故障。所提方法可适应列车不同运营速度、不同线路下的监测诊断需求，对保障高速列车安全运行、实现轴箱轴承的预测性延寿维护具有重要意义。

Abstract

To overcome the fluctuations in health monitoring indicators of axle box bearings in high-speed trains due to variations in operating lines and speeds

and to address the difficulties of traditional threshold methods in monitoring bearing health based on a unified standard

a threshold adaptive intelligent health monitoring method for axle box bearings under multiple operating conditions is proposed. First

by analyzing the Spearman correlation coefficients between features and rotational speed

the health indicators of the bearings at multiple operating speeds are normalized to eliminate fluctuations caused by changes in train speed. This enables the automatic adaptation of monitoring thresholds to the train's operating conditions. Next

an improved Isolation Forest algorithm based on random feature subsets and principal component direction segmentation (SPCD-iForest) is established. This algorithm uses the collaborative information provided by multidimensional features to classify the normal and faulty states of the bearings along the principal component direction of the data

enhancing the computational efficiency of anomaly detection while maintaining monitoring accuracy. Finally

the proposed intelligent health monitoring method is validated using data from line tests of train axle box bearings. Results show that the proposed method eliminates the impact of changes in train operating conditions on the health indicators of axle box bearings. The output anomaly factor is a dimensionless indicator ranging from 0 to 1

effectively reflecting changes in the health status of the axle box bearings. Compared to the TADS trackside acoustic detection method

it can detect bearing faults more than 10 days in advance and is adaptable to monitoring and diagnostic needs across different train speeds and lines. This method is significant for ensuring the safe operation of high-speed trains and enabling predictive maintenance for axle box bearings.

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references

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