Feature extraction method of vibration energy of rail vehicle running gear based on CVMD and FastICA
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摘要: 针对干扰状态下非线性信号的处理，提出一种结合基于相关性系数变分模态分解 (CVMD)与独立成分分析 (FastICA)的振动能量与特征提取方法，兼顾平稳信号与非平稳信号。走行部振动仿真和现场试验分析，验证了地铁车辆走行部主要能量分布在50~120 Hz，识别出走行部关键特征频率分别为75和100 Hz。研究结果可为地铁列车走行部实际工程环境的振动能量回收和特征提取提供理论基础。
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关键词:
- 地铁车辆 /
- 振动能量提取 /
- 相关性系数变分模态分解 /
- 快速独立成分分析
Abstract: To address the processing of nonlinear signals under interference, a vibration energy and feature extraction method integrating correlation coefficient-based variational modal decomposition (CVMD) and fast independent component analysis (FastICA) was proposed, which effectively handles both stationary and non-stationary signals. Through simulations and field test analyses of running gear vibration, it is verified that the main energy distribution of rail vehicle running gear is distributed within 50~120 Hz, and the key characteristic frequencies are identified as 75 and 100 Hz. The finding provide a theoretical basis for vibration energy recovery and feature extraction in the actual engineering applications of metro trains. -
图 2 VMD、ICEEMDAN与CVMD频域分解对比
Figure 2. Comparison of VMD、ICEEMDAN and CVMD for decomposing IMFs
图 3 走行部振动仿真信号的归一化快速谱峭度图
Figure 3. Normalized fast spectral kurtosis of simulation vibration signals for running gear
图 4 轨道车辆走行部综合试验系统
Figure 4. Comprehensive experiment system for rail vehicle running gear
图 5 走行部振动试验台信号的归一化快速谱峭度图
Figure 5. Normalized fast spectral kurtosis of test bench vibration signals for running gear
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