Unsupervised Adversarial Adaptation Network for Intelligent Fault Diagnosis

The data distribution shift is inevitable in practical fault diagnosis due to internal and outside changes of equipment. These obstacles will lead to performance degrade or even failure of diagnostic models. In light of these problems, a novel unsupervised intelligent diagnostic framework named Adversarial Adaptation network based on Classifier Discrepancy (AACD) is introduced for mechanical fault diagnosis where the training and test domains have different data distribution. Specifically, AACD mainly contains two parts: one is the shared feature generator built by the 1-D convolutional neural network and the other is double task-specific classifiers. They play an adversarial training game to learn class-separable and domain-invariant features for fault diagnosis. The proposed AACD is evaluated by 15 transfer diagnosis tasks constructed on the planetary gearbox test-bed and the rolling bearing test-bed. Moreover, six popular algorithms are selected for comparison. The comprehensive results validate the effectiveness and superiority of the proposed approach.