基于改进经验小波变换的机车轴承故障诊断

摘要/Abstract

摘要： 机车轴承在噪声较大的背景下工作，发生故障时，难以有效地提取其故障特征，针对这一问题，提出了经验小波变换（EWT）方法。为克服经验小波变换方法中噪声分量干扰子频带划分的问题，提出一种采用信号时频峭度谱局部极小值划分频带的方法，基于子频带构造正交小波滤波器组对信号进行EWT分解。仿真实验和工程应用表明，改进后的EWT能够较好地克服噪声分量对子频带划分的干扰，有效地分离出机车轴承损伤故障的特征。

关键词: 经验小波变换, 时频峭度谱, 子频带边界, 特征频率, 特征提取

Abstract: Locomotive bearings worked in the background of loud noise, it was difficult to extract the fault characteristics effectively. To solve this problem, an EWT was proposed. In order to overcome the problem of noise component interference sub-band divisions in EWT method, an approach was proposed, which used local minimum on a kind of time-frequency kurtosis spectrum of analyzed signals to make the frequency-band partitions, and orthogonal wavelet filter group was constructed based on the sub-bands to perform EWT decomposition. Simulation experiments and engineering applications show that the improved EWT may better overcome the interference of noise components on sub-band divisions, and effectively extract the defect features of locomotive bearings.

Key words: empirical wavelet transform(EWT), time-frequency kurtosis spectrum, boundary of sub-band, characteristic frequency, feature extraction

中图分类号:

TH113
TH17

段晨东;张荣. 基于改进经验小波变换的机车轴承故障诊断[J]. 中国机械工程.

DUAN Chendong;ZHANG Rong. Locomotive Bearing Fault Diagnosis Using an Improved Empirical Wavelet Transform[J]. China Mechanical Engineering.

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