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

• 作者: Yixiao Liao Ruyi Huang Jipu Li Zhuyun Chen Weihua Li
• 来源期刊: 中国机械工程学报
• 年，卷（期）: 2021,（3）
• 所属期刊栏目: Special Issue on AI-Enabled Monitoring Diagnosis & Prognosis
• 页码范围: 94-103
• 页数: 10页
• 语种: 英文

摘要

In machinery fault diagnosis,labeled data are always difficult or even impossible to obtain.Transfer learning can lever-age 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.How-ever,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 pro-posed 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 influ-ences of marginal and conditional distribution and adapt target domain with source domain.The experimental evalu-ation on cross domain bearing fault diagnosis demonstrates that DDATN can outperformance the state-of-the-art cross domain fault diagnosis methods.