A multi-source domain information fusion network for rotating machinery fault diagnosis under variable operating conditions

Abstract

In practical industrial scenarios, the variations of operating conditions such as load and rotational speed make mechanical systems subject to complex and variable environmental stresses, resulting in the distribution discrepancies of sample data. With the advantages of integrating the feature information and diagnosis knowledge, the transfer learning technique based on multiple source domains has become a stable and efficient solution to address the fault diagnosis challenge under variable operating conditions in the modern intelligent operation and maintenance. For the above discussions, a multi-source domain information fusion network (MDIFN) is proposed in this paper to obtain generalized knowledge with abundant feature information by combining the adversarial transfer learning technique with fine-grained information fusion of multiple source domains. First, an adversarial transfer network architecture is constructed in accordance with the complex feature transformation and the boundary equilibrium domain discrimination to implement feature learning and knowledge transfer of source and target domains. Then, a joint distribution domain adaptation mechanism is proposed to further facilitate the acquisition of domain invariant features. Finally, a class-related decision fusion (CDF) strategy is designed to realize the information fusion within the decision space. The fault diagnosis of rotating machinery under unknown operating conditions can be achieved by employing data under known multiple operating conditions for MDIFN training. The public Paderborn University (PU) bearing dataset and the actual mechanical comprehensive diagnosis simulation platform (MCDSP) bearing dataset from different testing rigs are considered to evaluate the cross-domain fault diagnosis performance of this method. The experimental results indicate that the method achieves an average accuracy of 95.97% on the PU dataset and 98.31% on the MCDSP dataset, which is superior to other state-of-the-art cross-domain fault diagnosis algorithms.