Deep Adversarial Capsule Network for Compound Fault Diagnosis of Machinery Toward Multidomain Generalization Task

Abstract

With advanced measurement technologies and signal analytics algorithms developed rapidly, the past decades have witnessed large amount of successful breakthroughs and applications in the field of intelligent fault diagnosis (IFD). However, the historical IFD methods have difficulties for compound fault diagnosis, when labeled data cannot be collected in advance for new or extreme working conditions. Facing with such challenges, a deep adversarial capsule network (DACN) is proposed to embed multidomain generalization into the intelligent compound fault diagnosis, so that the DACN not only can intelligently decouple the compound fault into multiple single faults for industrial equipment (IE) but also can be generalized from certain working conditions to another new. First, a DACN including feature extractor (FE), decoupling classifier (DC) and multidomain classifier (MC) is constructed for feature learning, fault decoupling, and unsupervised multidomain adaptation, respectively. Second, adversarial training is introduced into the DACN in the training stage via a gradient reversal layer that can build the connection between the FE and MC, which can force the DACN to learn the domain-invariance features. Finally, the DACN is trained using the single-fault data collected under multiple working conditions, and then applied to monitor the health condition of IE under new working conditions. The cross-validation experiments have been implemented on an automobile transmission (AT), which illustrates that the DACN obtains an optimum performance with the highest average accuracy of 97.65% for compound fault diagnosis of IE under multidomain generalization task and outperforms other related methods.