Collaborative and adversarial deep transfer auto-encoder for intelligent fault diagnosis

Abstract

Deep transfer learning provides an advanced analytical tool for intelligent fault diagnosis to learn shared fault knowledge in industrial scenarios whereby datasets are collected from different operating conditions. The majority of previous approaches consist in minimizing the domain discrepancy at the domain level, but they may cause erroneous mappings for local category features. Recently, to prioritize the feature discriminability, several adversarial learning approaches with bi-classifier have been developed by using the classification disagreement. However, they suffer from a restricted representation of the classification disagreement, and may fail to align category-level features when domain gaps are large. To address these weaknesses, we proposed a Collaborative and Adversarial Deep Transfer model based on a convolutional Auto-encoder (CADTA) for intelligent fault diagnosis. Specifically, by leveraging a couple of multi-task classifiers, a joint subspace feature discrimination method involving duplex adversarial learning is proposed to promote the category-level feature discriminability. Then, a collaborative transfer scheme is built to integrate the domain similarity learning and the joint subspace feature discrimination hierarchically into a convolutional Auto-encoder, in which the domain similarity learning is achieved by adapting the intermediate feature representations in terms of high-order moments. For the sake of efficient model training, a stage-wise adversarial training process is presented correspondingly. Extensive experiments of diverse transfer tasks based on two rolling bearing datasets and three transfer fault diagnosis cases demonstrate that CADTA outperforms the existing state-of-the-art deep transfer learning methods.