Gearbox Fault Diagnosis based on Time-frequency Combination Feature Extraction and Manifold Learning of MS-LTSA

Lingjun Xiao; Lü Yong; Rui Yuan

doi:10.16578/j.issn.1004.2539.2022.03.022

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Gearbox Fault Diagnosis based on Time-frequency Combination Feature Extraction and Manifold Learning of MS-LTSA

Development·Application | 更新时间：2022-09-24

- Gearbox Fault Diagnosis based on Time-frequency Combination Feature Extraction and Manifold Learning of MS-LTSA
- Journal of Mechanical Transmission Vol. 46, Issue 3, Pages: 140-148(2022)
- 作者机构：
  
  1.武汉科技大学冶金装备及其控制教育部重点实验室，湖北武汉 430081
  2.武汉科技大学机械传动与制造工程湖北省重点实验室，湖北武汉 430081
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2022.03.022
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肖凌俊,吕勇,袁锐.基于时频联合特征提取与MS-LTSA流形学习的齿轮箱故障诊断[J].机械传动,2022,46(03):140-148.

Xiao Lingjun,Yong Lü,Yuan Rui.Gearbox Fault Diagnosis based on Time-frequency Combination Feature Extraction and Manifold Learning of MS-LTSA[J].Journal of Mechanical Transmission,2022,46(03):140-148.
肖凌俊,吕勇,袁锐.基于时频联合特征提取与MS-LTSA流形学习的齿轮箱故障诊断[J].机械传动,2022,46(03):140-148. DOI： 10.16578/j.issn.1004.2539.2022.03.022.

Xiao Lingjun,Yong Lü,Yuan Rui.Gearbox Fault Diagnosis based on Time-frequency Combination Feature Extraction and Manifold Learning of MS-LTSA[J].Journal of Mechanical Transmission,2022,46(03):140-148. DOI： 10.16578/j.issn.1004.2539.2022.03.022.

摘要

提出了基于时频联合（TFC）特征提取和改进的监督局部切空间排列（MS-LTSA）的流形学习的齿轮箱故障诊断的方法。首先，提出了信号的时域、频域和HHT时频域三者结合的特征提取方法，以获取振动信号全面的特征向量信息；然后，提取高维特征向量的奇异值，利用流形学习理论对奇异值矩阵进行降噪；最后，通过降噪后的特征向量实现对齿轮箱各种故障的高效、精确地故障识别。提出的MS-LTSA方法实现了数据集内部结构信息和类判别信息的结合，提高了所提取低维特征的聚类效果；通过实验数据的分析，证实了所提方法在齿轮箱诊断上的优异表现和应用价值。

Abstract

A fault diagnosis method of gearbox based on time frequency union （TFC） feature extraction and manifold learning of improved supervised local tangent space arrangement （MS-LTSA） is presented. Firstly，a feature extraction method combining time domain，frequency domain and HHT time-frequency domain is proposed to obtain the comprehensive feature vector information of vibration signals. Then，the singular values of high-dimensional feature vectors are extracted and the singular value matrix is denoised by manifold learning theory. Finally，an efficient and accurate fault identification of the gearbox is realized by the feature vector after noise reduction. The proposed MS-LTSA method realizes the combination of the internal structure information and the class discrimination information of the data set，and improves the clustering effect of the extracted low dimensional features. Through analysis of experimental data，the excellent performance and application value of the proposed method in gearbox diagnosis are verified.

关键词

齿轮箱特征提取流形学习故障诊断

Keywords

GearboxFeature extractionManifold learningFault diagnosis

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