基于全矢排列熵的齿轮故障特征提取方法研究

PDF(1672 KB)

振动与冲击 ›› 2016, Vol. 35 ›› Issue (11) : 224-228.

论文

郝旺身，王洪明，董辛旻，郝伟，韩捷，张坤

作者信息 +

Gear Fault Feature Extraction Based on Full Vector Permutation Entropy

Hao Wangshen, Wang Hongmin,Dong Xinmin, Hao Wei, Han Jie, Zhang Kun

Author information +

文章历史 +

摘要

针对齿轮的故障振动信号的非平稳、非线性特征，采用非线性信号分析方法排列熵算法计算振动信号的排列熵大小来反映信号的复杂度。单通道的信息源难以反映出设备的真实运行状态，采用同源信息融合技术对双通道振动信号进行同源信息融合，计算融合后的信号的排列熵，进而提出了一种基于全矢排列熵（FVPE）的齿轮故障特征提取方法，通过实验模拟齿根裂纹、断齿和缺齿这三种故障状态，实验结果表明本方法有效地解决了单一通道信息源不完善造成的误诊难题，并可以很好地区分三种故障。

Abstract

Gear fault vibration signals are often non-stationary and non-linear, and permutation entropy can well reflect the degree of disorder of a one-dimensional time series and the dynamics of the mutant signal. However, the traditional fault diagnosis method based on a single source of vibration signal can’t ensure the integrity of the information. This article will apply permutation entropy algorithm to the two-channel homologous signal, presenting a method of gear fault feature extraction based on full vector permutation entropy. Experimental results show that this method can effectively reflect the mutation of signals, avoid misdiagnosis caused by a single channel information which is imperfect.

导出引用

郝旺身，王洪明，董辛旻，郝伟，韩捷，张坤 . 基于全矢排列熵的齿轮故障特征提取方法研究[J]. 振动与冲击, 2016, 35(11): 224-228

Hao Wangshen, Wang Hongmin,Dong Xinmin, Hao Wei, Han Jie, Zhang Kun. Gear Fault Feature Extraction Based on Full Vector Permutation Entropy[J]. Journal of Vibration and Shock, 2016, 35(11): 224-228

参考文献

[1] PINCUS S M. Approximate entropy as a measure of system ,complexity[J].Proc.Natl.Acad.Sci.USA,1991,88(6):2297-2301.
[2] Bandt C, Pome B.Permutation entropy:a natural complexity measure for time series[J].Physical Review Letters,2002,88(17):174102-1-174102-4.
[3]刘永斌. 基于非线性信号分析的滚动轴承状态监测诊断研究[D].中国科学技术大学,2011.
Liu Yongbin. Nonlinear signal analysis for rolling bearing condition monitoring and fault diagnosis[D]. University of Science and Technology of China,2011
[4]李凌均,韩捷,李朋勇,郝伟,陈磊. 矢双谱分析及其在机械故障诊断中的应用[J]. 机械工程学报,2011,47(17):50-54.
LI Lingjun, HAN Jie, LI Pengyong, HAO Wei, CHEN Lei. Vector-bispectrum Analysis and Its Application in Machinery Fault Diagnosis[J]. Chinese Journal of Mechanical Engineering,2011,47(17):50-54.
[5]韩捷,石来德.旋转机械的全信息能量谱分析方法研究[J].机械强度,2003,25(4):364-368.
HAN Jie, SHI Laide. Study of full information energy spectrum analysis method of rotary machinery[J]. Journal of Mechanical Strength,2003,25(4):364-368.
[6]冯辅周,饶国强,司爱威,吴广平. 排列熵算法研究及其在振动信号突变检测中的应用[J].振动工程学报,2012,25(2):221-224.
Feng Fuzhou, Rao Guoqiang, Si Aiwei, Wu Guangping. Permutation entropy algorithm and its application in the vibration signal mutation detection[J]. Journal of Vibration Engineering,2012,25(2):221-224
[7]冯辅周,饶国强,司爱威. 基于排列熵和神经网络的滚动轴承异常检测与诊断[J]. 噪声与振动控制,2013,33(3):212-217.
FENG Fuzhou, RAO Guoqiang, SI Aiwei. Abnormality Detection and Diagnosis of Rolling Bearing Based on Permutation Entropy and Neural Network[J]. Noise and vibration control, 2013,33(3):212-217.
[8]刘永斌,龙潜,冯志华,刘维来. 一种非平稳、非线性振动信号检测方法的研究[J]. 振动与冲击,2007,26(12):131-134.
LIU Yongbin,LONG Qian,FENG Zhihua,LIU Weilai.Detection method for nonlinear and non-stationary signals[J].Journal of Vibration and Shock,2007,26(12):131-134.
[9]冯辅周,司爱威,饶国强,江鹏程. 基于小波相关排列熵的轴承早期故障诊断技术[J]. 机械工程学报,2012,48(13):73-79.
FENG Fuzhou,SI Aiwei, RAO Guoqiang, JIANG Pengcheng. Early Fault Diagnosis Technology for Bearing Based on Wavelet Correlation Permutation Entropy[J]. Chinese Journal of Mechanical Engineering,2012,48(13):73-79.
[10] 昊普特.风力涡轮机动力传动故障诊断仿真器（WTDS）.[EB/OL]. http://www.haopute.com/p-998.html. [2014.12.11].
[11]韩捷,巩晓赟,陈宏. 全矢谱技术在齿轮故障诊断中的应用[J]. 中国工程机械学报,2010,8(1):81-85.
HAN Jie,Gong Xiaoyun,Chen Hong.Applying full vector spectrum for gear fault diagnosis[J]. Chinese Journal of Construction Machinery,2010,8(1):81-85.