基于全矢排列熵的齿轮故障特征提取方法研究

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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。

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	郝旺身
	王洪明
	董辛旻
	郝伟
	韩捷
	张坤

关键词 ：非线性, 排列熵, 全矢排列熵, 故障特征, 齿轮

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.

Key words： non-linear permutation entropy full vector permutation entropy fault feature gear

收稿日期: 2014-12-03 出版日期: 2016-05-25

引用本文:

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. JOURNAL OF VIBRATION AND SHOCK, 2016, 35(11): 224-228.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2016/V35/I11/224

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