A New Multiple Source Domain Adaptation Fault Diagnosis Method Between Different Rotating Machines

Abstract

Fault diagnosis based on data-driven methods are widely investigated when enough supervised samples of the target machine are available to build a reliable model. However, the labeled samples in practical operated machine are usually scarce and difficult to collect. If the model is built based on the sufficient labeled samples from different source machines, the diagnosis performance will degenerate owing to the domain discrepancy. To solve this issue, in this article, transfer learning (TL) is proposed by leveraging knowledge learned from source domain to target domain. While TL methods for fault diagnosis have been actively studied, most of them focus on learning from a single source. Since the labeled samples can come from multiple domains, more general diagnosis knowledge can be learned, which is beneficial to the prediction for the target domain. Therefore, a new TL approach based on multisource domain adaptation is proposed. A multiadversarial learning strategy is utilized for obtaining feature representations, which are invariant to the multiple domain shifts and discriminative for the learning goal at the same time. Extensive experimental analysis on four different bearing datasets is performed to illustrate the effectiveness and advantage of the proposed method.