Fusing joint distribution and adversarial networks: A new transfer learning method for intelligent fault diagnosis

Abstract

As integral components within rotating machinery, bearings and gears pose a critical challenge in fault diagnosis. Presently, data-driven fault diagnosis stands out as a viable approach. However, real-world operational variations readily induce domain shifts, complicating transfer learning and diminishing the diagnostic efficacy of models. The process of re-labeling the fault categories of model demands substantial time and financial resources. Consequently, to surmount these challenges, this study introduces a novel unsupervised transfer learning framework that leverages the amalgamation of joint distribution and adversarial networks for diagnosing faults in bearings and gears within rotating machinery.The joint adaptation network facilitates the learning of the transfer network by aligning the joint distribution across multiple specific domain layers. This alignment is achieved through the application of joint maximum mean discrepancy (JMMD) within the joint network. Simultaneously, the adversarial network employs a domain classifier to minimize the domain classification loss, treating it as the difference in domain distribution to mitigate domain shift effectively. The integration of these two methodologies accomplishes domain alignment, reduces model training time, and enhances the accuracy and stability of the diagnostic model.Validation of the proposed model framework is conducted using four sets of bearing faults and six sets of gear faults. The results confirm the superior accuracy and stability of the new model framework in addressing bearing and gear faults within the realm of rotating machinery.