Deep Multiadversarial Conditional Domain Adaptation Networks for Fault Diagnostics of Industrial Equipment

Deep learning methods of fault diagnostics require the availability of a large amount of labeled data for training, i.e., signal values corresponding to known degradation and fault states. Furthermore, the distribution of the training data should be similar to that of the (test) data collected in the field. Since these conditions are typically not satisfied in most industrial applications, this article develops a deep multiadversarial conditional domain adaptation network. The main original contribution lies in a novel method to align, class by class, the weighted marginal data distributions using multiple domain discriminators. The network allows overtaking the classification underperformance caused by the problem of negative transfer, which is typically encountered when only few training data of some of the classes are available. The proposed method is shown to outperform other state-of-the-art methods on two cross-domain fault diagnostic case studies, verified by applying Friedman and Holm post-hoc tests.