基于白噪声统计特性与EEMD的高速列车横向减振器故障诊断
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摘要
针对高速列车横向减振器故障信号非线性非平稳的特点,提出了基于白噪声统计特性与聚合经验模态分解(EEMD)相结合的故障诊断算法。利用经验模态分解(EMD)对故障信号进行去噪,然后对去噪后的信号进行EEMD分解,最后对用相关系数求得的最能反映振动信号的本征模态函数(IMF)计算排列组合熵。在240 km/h速度下,对高速列车横向减振器七种工况进行诊断,识别率达到91.8%。实验结果表明,与基于小波熵特征分析的算法相比,该算法具有更高的识别率和更强的抗噪性能。
Considering the nonlinearity and nonstationarity of the lateral damper fault signal of high-speed train,this paper proposed a fault diagnosis method by combining ensemble empirical mode decomposition( EEMD) with the characteristics of white noise. Using empirical mode decomposition( EMD) to denoise the original fault signal,then decomposing the denoised signal with EEMD. It calculated the permutation entropy of the IMF which best fitted the original signal based on correlation analysis. The recognition rate reached 91. 8% when applying this algorithm to diagnose seven different lateral damper faults of the high-speed train at the speed of 240 km / h. The experimental results show that the algorithm has higher recognition rate and stronger anti-noise performance comparing with wavelet entropy feature analysis method.
Considering the nonlinearity and nonstationarity of the lateral damper fault signal of high-speed train,this paper proposed a fault diagnosis method by combining ensemble empirical mode decomposition( EEMD) with the characteristics of white noise. Using empirical mode decomposition( EMD) to denoise the original fault signal,then decomposing the denoised signal with EEMD. It calculated the permutation entropy of the IMF which best fitted the original signal based on correlation analysis. The recognition rate reached 91. 8% when applying this algorithm to diagnose seven different lateral damper faults of the high-speed train at the speed of 240 km / h. The experimental results show that the algorithm has higher recognition rate and stronger anti-noise performance comparing with wavelet entropy feature analysis method.
引文
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[13]张文忠,周蓉,武旭红,等.利用白噪声分解特性的EEMD阈值降噪方法[J].测绘科学技术学报,2013,30(3):255-259.
[14]石国良,李晓,金炜东,等.基于排列组合熵的高速列车走行部故障分析[J].计算机应用研究,2014,31(12):3625-3627.
[15]孙克辉,谈国强,盛利元.基于排列熵算法的混沌伪随机序列复杂性分析[J].计算机工程与应用,2009,44(3):47-49.
[2]大庭拓也,蔡千华.基于振动分析的新干线转向架的状态监视[J].国外铁道车辆,2013,50(3):32-40.
[3]秦娜,金炜东,黄进,等.基于EEMD样本熵的高速列车转向架故障特征提取[J].西南交通大学学报,2014,49(1):33-38.
[4]Wu Zhaohua,Huang N E.Ensemble empirical mode decomposition:a noise-assisted data analysis method[J].Advances in Adaptive Data Analysis,2009,28(9):1-41.
[5]曹冲锋,杨世锡,杨将新.大型旋转机械非平稳振动信号的EEMD降噪方法[J].振动与冲击,2010,46(3):65-70.
[6]秦娜,金炜东,黄进,等.高速列车转向架故障信号的小波熵特征分析[J].计算机应用研究,2013,30(12):3657-3659.
[7]戴吾蛟,丁晓利,朱建军,等.基于经验模式分解的滤波去噪法及其在GPS多路径效应中的应用[J].测绘学报,2006,35(4):321-327.
[8]曹冲锋,杨世锡,杨将新.基于白噪声统计特性的振动模式提取方法[J].机械工程学报,2010,46(3):65-70.
[9]陈仁祥,汤宝平,马婧华.基于EEMD的振动信号自适应降噪方法[J].振动与冲击,2012,31(15):82-86.
[10]Chang C C,Lin C J.LIBSVM:a library for support vector machines[J].ACM Trans on Intelligent Systems and Technology,2011,2(3):1-27.
[11]张学工.关于统计学习理论与支持向量机[J].自动化学报,2000,26(1):32-42.
[12]陈仁祥,汤宝平,马婧华.基于EEMD的振动信号自适应降噪方法[J].振动与冲击,2012,31(15):82-86.
[13]张文忠,周蓉,武旭红,等.利用白噪声分解特性的EEMD阈值降噪方法[J].测绘科学技术学报,2013,30(3):255-259.
[14]石国良,李晓,金炜东,等.基于排列组合熵的高速列车走行部故障分析[J].计算机应用研究,2014,31(12):3625-3627.
[15]孙克辉,谈国强,盛利元.基于排列熵算法的混沌伪随机序列复杂性分析[J].计算机工程与应用,2009,44(3):47-49.