LOW FREQUENCY FAULT FEATURE EXTRACTION FOR GEARBOX BASED ON WAVELET TRANSFORM AND CONSTRAINED INDEPENDENT COMPONENT ANALYSIS

LENG JunFa; WANG ZhiYang; CHEN HuiTao; JING ShuangXi

doi:10.16579/j.issn.1001.9669.2018.05.004

您当前的位置：

首页 >

文章列表页 >

LOW FREQUENCY FAULT FEATURE EXTRACTION FOR GEARBOX BASED ON WAVELET TRANSFORM AND CONSTRAINED INDEPENDENT COMPONENT ANALYSIS

更新时间：2022-09-22

- LOW FREQUENCY FAULT FEATURE EXTRACTION FOR GEARBOX BASED ON WAVELET TRANSFORM AND CONSTRAINED INDEPENDENT COMPONENT ANALYSIS
- Journal of Mechanical Strength Vol. 40, Issue 5, Pages: 1037-1042(2018)
- 作者机构：
  
  1. 河南理工大学机械与动力工程学院
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2018.05.004
  CLC：
扫描看全文
LENG JunFa, WANG ZhiYang, CHEN HuiTao, et al. LOW FREQUENCY FAULT FEATURE EXTRACTION FOR GEARBOX BASED ON WAVELET TRANSFORM AND CONSTRAINED INDEPENDENT COMPONENT ANALYSIS. [J]. 40(5):1037-1042(2018)
DOI：

LENG JunFa, WANG ZhiYang, CHEN HuiTao, et al. LOW FREQUENCY FAULT FEATURE EXTRACTION FOR GEARBOX BASED ON WAVELET TRANSFORM AND CONSTRAINED INDEPENDENT COMPONENT ANALYSIS. [J]. 40(5):1037-1042(2018) DOI： 10.16579/j.issn.1001.9669.2018.05.004.

摘要

齿轮箱低频故障信号易受到高频振动及强噪声等因素的干扰,测量信号中隐藏的有效低频故障特征比较微弱,再考虑到源噪声的影响,则约束独立分量分析直接提取低频故障特征的效果很差。为此提出了基于小波变换与约束独立分量分析的齿轮箱低频故障特征提取方法,通过对测量信号的小波多分辨率分解,有针对性地选择小波系数重构,提高了信噪比和非高斯性,从而增强了约束独立分量分析方法提取齿轮箱低频故障特征的效果。而未经小波变换除噪时,约束独立分量分析的效果不佳。断齿与局部断齿的低频故障特征提取试验结果表明,提出的方法能有效降低高频振动及源噪声等因素的干扰,提取出齿轮箱的低频故障特征,尤其是微弱低频故障特征,为齿轮箱低频故障特征提取与故障诊断提供了一种有效方法。

Abstract

The effective low frequency fault feature hidden in gearbox measured signal is very weak for the influence of high frequency vibration and strong noise,and considering source noise,the extraction effect of low frequency fault feature with constrained independent component analysis（ c ICA） method directly is very poor. Aiming at this problem,a method of gearbox low frequency fault feature extraction based on wavelet transform and c ICA is proposed. This method can improve signal-to-noise ratio（ SNR） and nongaussianity of the analyzed signal,and enhance analysis effect of c ICA algorithm through wavelet multiresolution decomposition for the measured signal and reconstruction for some wavelet coefficients. But the analysis effect is not good for the c ICA method without WT denosing. Low frequency fault feature extraction experiments with a missing tooth and a chipped tooth were analyzed,and the results show that this method can effectively reduce the influence of high frequency vibration and source noise,and extract gearbox low frequency fault feature,especially weak low frequency fault feature. The proposed method provides an effective approach for low frequency fault feature extraction and fault diagnosis of gearbox.

关键词

齿轮箱低频故障特征提取小波变换约束独立分量分析

Keywords

GearboxLow frequency fault feature extractionWavelet transform（WT）Constrained independent component analysis（cICA）

references

Views

121

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

FAULT DIAGNOSIS OF GEARBOX BASED ON ADAPTIVE EEMD AND FAST KURTOGRAM

RESEARCH ON GEAR BOX FAULT DIAGNOSIS BASED ON DCNN AND XGBOOST ALGORITHM

ROTATING MACHINERY FAULT DIAGNOSIS BASED ON TWO-DIMENSIONAL CONVOLUTION NEURAL NETWORK

FAULT FEATURE EXTRACTION OF VIRIABLE RORATION SPEED GEARBOX GEAR BASED ON VMD AND ORDER TRACKING

RESEARCH ON GEARBOX FAULT DIAGNOSIS BASE ON ISOMAP AND IGA-SVM

Related Author

No data

Related Institution

College of Mechanical Engineering,Liaoning Technical University

College of Mechanical Engineering,Shenyang Ligong University

Shanghai Dian Ji University

China Nuclear Industry Maintenance Co.,Ltd.,Haiyan Branch

Mechanical and Automotive Engineering,Qingdao University of Technological

⁰