电动汽车电驱动系统齿轮非平稳故障特征提取

doi:10.3901/JME.2025.20.223

机械工程学报 ›› 2025, Vol. 61 ›› Issue (20): 223-233.doi: 10.3901/JME.2025.20.223

• 运载工程 • 上一篇

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电动汽车电驱动系统齿轮非平稳故障特征提取

刘坤¹, 吴维¹, 陈星², 苑士华¹

1. 北京理工大学机械与车辆学院北京 100080;
2. 重庆文理学院智能制造工程学院重庆 402160

收稿日期:2024-10-21 修回日期:2025-06-05 发布日期:2025-12-03
作者简介:刘坤，男，1998年出生，博士研究生。主要研究方向为电驱动系统故障诊断。E-mail:liukun056012@bit.edu.cn
吴维(通信作者)，男，1983年出生，博士，教授，博士研究生导师。主要研究方向为车辆动力学与控制和车辆流体传动与控制等。E-mail:wuweijing@bit.edu.cn
基金资助:
国家自然科学基金资助项目(U1864210)。

Non-stationary Fault Feature Extraction for Gear in Electric Powertrain of Electric Vehicle

LIU Kun¹, WU Wei¹, CHEN Xing², YUAN Shihua¹

1. School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100080;
2. School of Intelligent Manufacturing Engineering, Chongqing University of Arts and Sciences, Chongqing 402160

Received:2024-10-21 Revised:2025-06-05 Published:2025-12-03

摘要/Abstract

摘要： 为了克服车载环境下电驱动系统转速信号获取困难，导致在非平稳工况下难以提取电驱动系统齿轮故障特征的问题，基于电驱动系统电机和传动机构一体化的结构特点，提出一种结合同步压缩变换和角频域稀疏分解的非平稳工况下齿轮故障特征提取方法。首先，对振动加速度信号进行同步压缩变换，提升开关频率成分时频聚集性，获取准确的时频脊线。其次，基于开关频率成分时频脊线计算电机转频，进而得到传动机构每轴转频，根据转频信号进行角域重采样。最后，建立基于广义极小极大凹罚函数的稀疏分解模型，在角频域重构齿轮啮合阶次及其调制阶次。通过仿真测试、试验平台测试和实车测试验证所提方法的准确性。结果表明，所提方法能够在仅使用振动加速度传感器的情况下提取齿轮非平稳故障特征，提高了电驱动系统齿轮故障诊断的效率。

关键词: 电驱动系统, 非平稳工况, 开关频率, 齿轮故障

Abstract: Due to the inability to obtain speed signals in the vehicle environment, it is difficult to extract gear fault features of the electric powertrain under non-stationary conditions. To address this issue, a gear vibration feature extraction method based on synchronous compression transformation and sparse decomposition in the angular frequency domain under non-stationary conditions is proposed. This method takes advantage of the integrated structure of electric powertrain. Firstly, synchronous compression transformation is performed on the vibration acceleration signal to improve the time-frequency aggregation of the switching frequency harmonics and obtain accurate time-frequency ridge. Secondly, based on the switching frequency harmonic time-frequency ridge, the motor speed is calculated to obtain the speed of each axis of the transmission. Based on the speed signal, angular domain sampling is conducted. Finally, a sparse decomposition model based on the generalized minimax concave penalty function is established. The gear meshing order and its modulation order are reconstructed in the angular frequency domain. The accuracy of the proposed method is verified through simulation, experimental platform, and vehicle. The results show that the proposed method can extract non-stationary fault features of gears using only vibration acceleration sensors, and improve the efficiency of gear fault diagnosis in electric powertrain.

Key words: electric powertrain, non-stationary condition, switching frequency, gear fault

中图分类号:

U469
TH165

刘坤, 吴维, 陈星, 苑士华. 电动汽车电驱动系统齿轮非平稳故障特征提取[J]. 机械工程学报, 2025, 61(20): 223-233.

LIU Kun, WU Wei, CHEN Xing, YUAN Shihua. Non-stationary Fault Feature Extraction for Gear in Electric Powertrain of Electric Vehicle[J]. Journal of Mechanical Engineering, 2025, 61(20): 223-233.

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电动汽车电驱动系统齿轮非平稳故障特征提取

Non-stationary Fault Feature Extraction for Gear in Electric Powertrain of Electric Vehicle

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