  The demand of artificial intelligent adoption for condition-based maintenance
strategy is astonishingly increased over the past few years. Intelligent fault
diagnosis is one critical topic of maintenance solution for mechanical systems.
Deep learning models, such as convolutional neural networks (CNNs), have been
successfully applied to fault diagnosis tasks for mechanical systems and
achieved promising results. However, for diverse working conditions in the
industry, deep learning suffers two difficulties: one is that the well-defined
(source domain) and new (target domain) datasets are with different feature
distributions; another one is the fact that insufficient or no labelled data in
target domain significantly reduce the accuracy of fault diagnosis. As a novel
idea, deep transfer learning (DTL) is created to perform learning in the target
domain by leveraging information from the relevant source domain. Inspired by
Wasserstein distance of optimal transport, in this paper, we propose a novel
DTL approach to intelligent fault diagnosis, namely Wasserstein Distance based
Deep Transfer Learning (WD-DTL), to learn domain feature representations
(generated by a CNN based feature extractor) and to minimize the distributions
between the source and target domains through adversarial training. The
effectiveness of the proposed WD-DTL is verified through 3 transfer scenarios
and 16 transfer fault diagnosis experiments of both unsupervised and supervised
(with insufficient labelled data) learning. We also provide a comprehensive
analysis of the network visualization of those transfer tasks.
