基于双树复小波的高速列车转向故障特征分析

Dual Tree Complex Wavelet Based Fault Characteristic Analysis for High-speed Trains

为实现对高速列车转向架特殊部件性能的及时检测诊断和故障排除,提高高铁运行过程的安全与舒适性,提出基于双树复小波特征提取的高速列车转向故障分析方法。基于双树复小波对信号进行提取,获得高速列车转向过程中故障信号所特有的波动信息和多尺度趋势,利用对小波系数进行Hilbert变换,获得每个时刻不重叠尺度区间的时长,从而获得高速列车转向过程中故障信息的特征信号的自校正性和高计算效率。最后,通过实验表明,所提算法的转向故障识别率会伴随行驶车速增加而同步提升,当速度大于200 km/h时,可获得90%以上故障识别性能,验证了所提算法的有效性。

In order to further realize the timely diagnosis and and fault diagnosis exclusion of the performance of high-speed train steering rack special components, and also the improvement of safety and comfort, a dual tree complex wavelet based fault characteristic analysis for high-speed trains is proposed. The characteristic of the signal is extracted based on dual tree complex wavelet to get he characteristic of wave information and multi-scale tendency of fault signals in the process of high-speed railway, and it uses the Hilbert transform of the wavelet coefficients to obtain the wavelet coefficients at each time interval, so as to obtain the self calibration and high computational efficiency of fault characteristic signals in the process of high-speed train steering. Finally, the experiments show that the proposed algorithm could get synchronous lifting of fault recognition rate with the increase of speed, when the speed is greater than 195 km/h, we can get above 90 % of the fault recognition performance, which verifies the effectiveness of the proposed algorithm.