Chinese Journal of Mechanical Engineering >

2021 , Vol. 34 >Issue 3: 52 - 52

DOI: https://doi.org/10.1186/s10033-021-00566-3

Special Issue on AI-Enabled Monitoring Diagnosis & Prognosis

Dynamic Distribution Adaptation Based Transfer Network for Cross Domain Bearing Fault Diagnosis

  • Yixiao Liao ,
  • Ruyi Huang ,
  • Jipu Li ,
  • Zhuyun Chen ,
  • Weihua Li
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  • School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou 510641, China

Received date: 2020-10-30

  Revised date: 2021-04-01

  Online published: 2021-12-21

Supported by

Supported by National Natural Science Foundation of China (Grant Nos. 51875208, 51475170), National Key Research and Development Program of China (Grant No. 2018YFB1702400)

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Abstract

In machinery fault diagnosis, labeled data are always difficult or even impossible to obtain. Transfer learning can leverage related fault diagnosis knowledge from fully labeled source domain to enhance the fault diagnosis performance in sparsely labeled or unlabeled target domain, which has been widely used for cross domain fault diagnosis. However, existing methods focus on either marginal distribution adaptation (MDA) or conditional distribution adaptation (CDA). In practice, marginal and conditional distributions discrepancies both have significant but different influences on the domain divergence. In this paper, a dynamic distribution adaptation based transfer network (DDATN) is proposed for cross domain bearing fault diagnosis. DDATN utilizes the proposed instance-weighted dynamic maximum mean discrepancy (IDMMD) for dynamic distribution adaptation (DDA), which can dynamically estimate the influences of marginal and conditional distribution and adapt target domain with source domain. The experimental evaluation on cross domain bearing fault diagnosis demonstrates that DDATN can outperformance the state-of-the-art cross domain fault diagnosis methods.

Key words： Cross domain fault diagnosis; Dynamic distribution adaptation; Instance-weighted dynamic MMD; Transfer learning

Cite this article

Yixiao Liao , Ruyi Huang , Jipu Li , Zhuyun Chen , Weihua Li . Dynamic Distribution Adaptation Based Transfer Network for Cross Domain Bearing Fault Diagnosis[J]. Chinese Journal of Mechanical Engineering, 2021 , 34(3) : 52 -52 . DOI: 10.1186/s10033-021-00566-3

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