基于改进变分模态分解和温振特征融合的高速列车齿轮箱轴承故障诊断方法
王连富1王梓帆2董俭雄2田光荣3
Fault Diagnosis Method for High-speed Train Gearbox Bearing Based on Improved VMD and Temperature-vibration Feature Fusion
WANG Lianfu1WANG Zifan2DONG Jianxiong2TIAN Guangrong3
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作者信息:1.中车长春轨道客车股份有限公司，130062，长春
2.西南交通大学牵引动力国家重点实验室，610031，成都
3.中国铁道科学研究院集团有限公司机车车辆研究所，100081，北京
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Affiliation:1.CRRC Changchun Railway Vehicles Co., Ltd., 130062, Changchun, China
2.State Key Laboratory of Traction Power, Southwest Jiaotong University, 610031, Chengdu, China
3.Locomotive and Car Research Institute, China Academy of Railway Sciences Group Co., Ltd., 100081, Beijing, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2024.07.004
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栏目/Col:学术专论
摘要:
［目的］我国现有的高速列车轴承故障监测和诊断多基于单一的温度或振动数据，单一的温度数据容易遗漏关键部件早期的故障信息，单一的振动数据较难对某些复杂耦合工况故障进行识别。因此，有必要结合温度与振动数据，研究温振特征融合的齿轮箱轴承故障诊断方法。［方法］为了确定VMD（变分模态分解）法的分解参数,引入加权峭度系数指标；结合 LMD（局域均值分解）法和VMD法，提出一种新的处理轴承原始振动数据、提取故障特征的方法；基于改进的 VMD法、LLE（局部线性嵌入）特征降维法和BP（反向传播）神经网络，提出一种温振特征融合的轴承故障诊断方法。以时域特征和温度特征作为输入，建立温振特征融合的轴承故障诊断模型。利用高速列车滚动轴承试验台，对国内某型高速动车组用齿轮箱轴承开展故障模拟试验，采集相关振动数据验证所提方法的有效性和可行性。［结果及结论］所提齿轮箱轴承故障诊断方法对齿轮箱轴承正常状态、外圈故障和滚动体故障的平均识别准确率均高于98%。
Abstracts:
［Objective］ Existing methods for high-speed train gearbox bearing monitoring and diagnosis in China often rely solely on temperature or vibration data. Rely solely on a single temperature data point may result in missing early fault information of key components, while only vibration data may struggle to support identification of faults under complex coupling conditions. Therefore, it is necessary to combine temperature and vibration data to develop a fault diagnosis method for gearbox bearings with temperature-vibration features. ［Method］ To determine the decomposition parameters of VMD (variational mode decomposition) method, a weighted kurtosis coefficient indicator is introduced. Combining LMD (local mean decomposition) and VMD methods, a new approach for processing raw vibration data and extracting fault features is proposed. Based on the improved VMD method, LLE (locally linear embedding) feature dimensionality reduction method, and BP (back-propagation) neural network, a method for temperature-vibration feature fusion in bearing fault diagnosis is proposed. Time-domain features and temperature features are used as inputs to establish the temperature-vibration feature fusion bearing fault diagnosis model. Using a high-speed train rolling bearing test bench, fault simulation tests are conducted on gearbox bearings of a certain type of high-speed EMU (electric multiple units) in China, and relevant vibration data are collected to validate the effectiveness and feasibility of proposed model. ［Result & Conclusion］ The proposed fault diagnosis method for gearbox bearings achieves an average identification accuracy of over 98% for normal state, outer ring fault, and rolling element fault.
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