Rolling Bearing Fault Diagnosis based on Wavelet and Deep Wavelet Auto-encoder

Xiaolei Du; Zhigang Chen; Nan Zhang; Xingguo Guo

doi:10.16578/j.issn.1004.2539.2019.09.017

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Rolling Bearing Fault Diagnosis based on Wavelet and Deep Wavelet Auto-encoder

Test·Analysis | 更新时间：2022-09-24

- Rolling Bearing Fault Diagnosis based on Wavelet and Deep Wavelet Auto-encoder
- Journal of Mechanical Transmission Vol. 43, Issue 9, Pages: 103-108(2019)
- 作者机构：
  
  1.北京建筑大学机电与车辆工程学院，北京 100044
  2.北京市建筑安全监测工程技术研究中心，北京 100044
  3.中国矿业大学机电工程学院，北京 100083
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2019.09.017
  CLC：
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杜小磊,陈志刚,张楠等.基于小波和深度小波自编码器的轴承故障诊断[J].机械传动,2019,43(09):103-108.

Du Xiaolei,Chen Zhigang,Zhang Nan,et al.Rolling Bearing Fault Diagnosis based on Wavelet and Deep Wavelet Auto-encoder[J].Journal of Mechanical Transmission,2019,43(09):103-108.
杜小磊,陈志刚,张楠等.基于小波和深度小波自编码器的轴承故障诊断[J].机械传动,2019,43(09):103-108. DOI： 10.16578/j.issn.1004.2539.2019.09.017.

Du Xiaolei,Chen Zhigang,Zhang Nan,et al.Rolling Bearing Fault Diagnosis based on Wavelet and Deep Wavelet Auto-encoder[J].Journal of Mechanical Transmission,2019,43(09):103-108. DOI： 10.16578/j.issn.1004.2539.2019.09.017.

摘要

针对滚动轴承故障严重程度与复合故障难以准确识别的问题，提出了一个基于提升双树复小波包（Lifting Dual-Tree Complex Wavelet Packet，LDTCWP）和深度小波自编码器（Deep Wavelet Auto-Encoder，DWAE）的轴承故障诊断方法。首先，使用迁移学习扩展目标数据量；其次，对轴承振动数据进行3层提升双数复小波包分解，分别计算各子频带信号的样本熵、排列熵和能量矩，作为初始特征向量；最后，将初始特征向量输入DWAE，进行二次特征提取并实现故障诊断。实验结果表明，该方法能有效地对滚动轴承进行多种故障类型和多种故障程度的识别，与传统机器学习方法相比，在目标数据较少的情况下也具有较强的泛化能力、特征提取能力和识别能力。

Abstract

Aiming at the problem that it is difficult to accurately identify the fault severities and compound faults of rolling bearings，a method based on lifting dual-tree complex wavelet packet（LDTCWP）and deep wavelet auto-encoder （DWAE） is proposed. Firstly，the transfer learning strategy is introduced to extend the target data amount. Secondly，the vibration data of bearings is decomposed into three layers via lifting dual-tree complex wavelet packet. The sample entropy，permutation entropy and energy moment of each sub-band are calculated as raw eigenvectors. Finally，the raw eigenvectors are sent into DWAE for quadratic feature extraction and fault diagnosis. The fault diagnosis experiment results show that the method can effectively identify multiple fault types and multiple fault severities of bearings. Compared with traditional machine learning methods，the proposed method has better generalization ability，feature extraction ability and recognition ability in the case of insufficient target vibration data.

关键词

滚动轴承提升双树复小波包深度小波自编码器迁移学习故障诊断

Keywords

Rolling bearingLifting dual-tree complex wavelet packetDeep wavelet auto-encoderTransfer learningFault diagnosis

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