A Deep Adversarial Transfer Learning Network for Machinery Emerging Fault Detection

Deep transfer learning has attracted many attentions in machine intelligent fault diagnosis. However, most existed deep transfer learning algorithms encounter difficulties to detect a new emerging fault in target domain because these methods assume that the source and target domains have the same fault categories. Unfortunately, in real-world applications, new fault may emerge during machine running, which is not the same as those faults for training diagnosis models. To solve this problem, a novel fault diagnosis method named deep adversarial transfer learning network (DATLN) is proposed for new emerging fault detection. First, a one-dimension convolutional neural network is constructed to learn invariant features from the raw vibration signals of the source and target domains. Then, a multiple label classifier is trained to recognize known fault classes of the source and target domains. Finally, a decision boundary is built for the new emerging fault detection by training a classifier to recognize some target samples as new ones. Experiments on rolling bearing and gearbox demonstrate that the DATLN can implement the faults recognition with high accuracy and outperform other transfer learning methods when a new fault emerging in the target domain.