A Deep Domain-Adversarial Transfer Fault Diagnosis Method for Rolling Bearing Based on Ensemble Empirical Mode Decomposition

In recent years, the deep learning-based fault diagnosis methods for rotating mechanical equipment have attracted great concern. However, because the data feature distributions present differences in applications with varying working conditions, the deep learning models cannot provide satisfactory performance of fault prediction in such scenarios. To address this problem, this paper proposes a domain adversarial-based rolling bearing fault transfer diagnosis model EMBRNDNMD. First of all, an EEMD-based time-frequency feature graph (EEMD-TFFG) construction method is proposed, and the time-frequency information of nonlinear nonstationary vibration signal is extracted; secondly, a multi-branch ResNet (MBRN) structure is designed, which is used to extract deep features representing the bearing state from EEMD-TFFG; finally, to solve the model domain adaptation transfer problem under varying working conditions, the adversarial network module and MK-MMD distribution difference evaluation method are introduced to optimize MBRN, so as to reduce the probability distribution difference between the deep features of source domain and target domain, and to improve the accuracy of EMBRNDNMD in state diagnosis of target domain. The results of experiments carried out on two bearing fault test platforms prove that EMBRNDNMD can maintain an average accuracy above 97% in fault transfer diagnosis tasks, and this method also has high stability and strong ability of scene adaptation.

Automated summary

In recent years, the deep learning-based fault diagnosis methods for rotating mechanical equipment have attracted attention. However, due to differences in data feature distributions under varying working conditions, these models cannot provide satisfactory fault prediction performance in such scenarios. To address this, this paper proposes a domain adversarial-based rolling bearing fault transfer diagnosis model EMBRNDNMD. The model uses an EEMD-based time-frequency feature graph construction method to extract time-frequency information and a multi-branch ResNet structure to extract deep features representing the bearing state. Additionally, an adversarial network module and MK-MMD distribution difference evaluation method are introduced to optimize the model and reduce the probability distribution difference between source and target domains, thus improving the accuracy of EMBRNDNMD in the target domain.