Bearing Fault Diagnosis Method Based on 2D Empirical Wavelet Transform Texture Domain Feature Adaptive Extraction

doi:10.7652/xjtuxb202112010

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Bearing Fault Diagnosis Method Based on 2D Empirical Wavelet Transform Texture Domain Feature Adaptive Extraction

更新时间：2025-07-09

- Bearing Fault Diagnosis Method Based on 2D Empirical Wavelet Transform Texture Domain Feature Adaptive Extraction
- Vol. 55, Issue 12, Pages: 79-86(2021)
- 作者机构：
  
  西安交通大学机械制造系统工程国家重点实验室,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202112010
  CLC： TH17
- Online First：10 December 2021，
  
  Published：2021
- 稿件说明：
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Bearing Fault Diagnosis Method Based on 2D Empirical Wavelet Transform Texture Domain Feature Adaptive Extraction[J]. 2021, 55(12): 79-86.
DOI：

Bearing Fault Diagnosis Method Based on 2D Empirical Wavelet Transform Texture Domain Feature Adaptive Extraction[J]. 2021, 55(12): 79-86. DOI： 10.7652/xjtuxb202112010.

摘要

针对滚动轴承变故障类型、变故障程度的复杂条件

为了获得更加丰富的故障信息

提出一种新的二维纹理域信号特征自适应提取方法。在新的二维纹理域构造方法中

将一维振动信号转换为二维纹理矩阵。经验证

构造的二维振动信号纹理域对于不同故障类型、不同故障程度的滚动轴承

均有较强的故障征兆能力。为了弥补直接从原始信号纹理提取特征时受限于纹理像素的缺点

提出一种基于二维经验小波变换的纹理域自适应提取方法。利用二维经验小波变换将二维振动信号纹理自适应分解为多个纹理分量

分别提取多尺度纹理特征

既考虑了宏观纹理

又兼顾了细节纹理

解决了纹理像素对纹理域提取的限制和影响

能准确提取到轴承振动信号的故障特征。利用支持向量机对不同故障程度、不同故障类型的滚动轴承进行识别

与不经过二维经验小波变换处理相比

识别准确率从19.8%提升至98.1%

验证了方法的有效性。所提方法适用于变故障类型和变故障程度复杂条件下的滚动轴承故障诊断。

Abstract

Aiming at the complex conditions of variable fault types and degrees of rolling bearings

a new adaptive feature extraction method for 2D texture domain signals is proposed to obtain more abundant fault information. In this newly proposed 2D texture domain construction method

1D vibration signal is transformed into a 2D texture matrix. It is proved that the constructed 2D vibration signal texture domain has strong fault symptom ability for rolling bearings with different fault types and different fault degrees. To make up the limitation of texture pixel size when extracting features directly from original signal texture

an adaptive texture-domain extraction method based on 2D empirical wavelet transform is introduced. Adopting the 2D empirical wavelet transform

the texture of 2D vibration signal is decomposed adaptively into multiple texture components

and multi-scale texture feature extraction is carried out respectively considering both the macro texture and the texture details

thus the restriction and influence of texture pixel size on extraction of texture domain are eliminated to accurately extract the bearing fault feature of vibration signal. The rolling bearings with different fault degrees and different fault types are identified by support vector machine. Compared with the method without 2D empirical wavelet transform

the proposed method improves the recognition accuracy from 19.8% to 98.1%

which verifies the effectiveness of this proposed method.

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references

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