基于多层混合滤噪的轴承早期弱故障特征提取方法

吕靖香 余建波

振动与冲击 ›› 2018, Vol. 37 ›› Issue (8) : 22-27.

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振动与冲击 ›› 2018, Vol. 37 ›› Issue (8) : 22-27.
论文

基于多层混合滤噪的轴承早期弱故障特征提取方法

  • 吕靖香 余建波
作者信息 +

Early fault diagnosis of bearings based on multilayer hybrid de-noising

  • LV Jingxiang,  YU Jianbo
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文章历史 +

摘要

为了提取受强背景噪声干扰的信号中的弱故障特征,提出一种基于局部均值分解的多层混合滤噪方法(Local mean decomposition- Multilayer hybrid de-noising, LMD-MHD)。针对LMD分解所得的乘积函数(Product function,PF)分量可能存在虚假分量的问题,提出一种多指标综合决策方法,结合各指标在不同故障阶段的量化能力,筛选出合理的有效PF分量。将小波阈值滤噪设为奇异值分解(Singular value decomposition,SVD)的前置处理单元,使保留的较大奇异值以特征信息贡献为主,减少干扰成分,并采用信号快速傅里叶变换结果中主频率个数来确定奇异值重构阶数。轴承早期故障振动信号的试验结果表明,该方法能够可有效滤除随机噪声和脉冲干扰,提取强背景噪声下的早期弱故障特征,提高轴承故障诊断的准确性。

Abstract

In order to extract fault features embedded in weak signals consisting of much noise, a new method named local mean decompositionmultilayer hybrid de-noising (LMD-MHD) was proposed. A series of product functions (PFs) were obtained after LMD. Multiple criteria decision was proposed to select the effective PF components reasonably, which combined quantitative ability of each index at different fault stages. Then, the wavelet threshold de-noising was used as the pre-filter process. The order of effective ranks was determined by the number of main frequency in the Fast Fourier transformation result of the signal. Experimental results on the actual bearing vibration signals demonstrate that this method can effectively remove interference of noise and extract the faint fault features. Thus, the proposed method can be used to improve the accuracy of bearing fault diagnosis.

关键词

弱故障诊断 / 局部均值分解 / 滤噪 / 奇异值分解

Key words

Faint fault diagnosis / Local mean decomposition / De-noising / Singular value decomposition

引用本文

导出引用
吕靖香 余建波. 基于多层混合滤噪的轴承早期弱故障特征提取方法[J]. 振动与冲击, 2018, 37(8): 22-27
LVJingxiang, YU Jianbo. Early fault diagnosis of bearings based on multilayer hybrid de-noising[J]. Journal of Vibration and Shock, 2018, 37(8): 22-27

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