Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis

TL;DR

This paper introduces WD-DTL, a novel transfer learning method using Wasserstein distance and adversarial training to improve fault diagnosis across different domains with limited labeled data.

Contribution

It proposes a new Wasserstein distance-based deep transfer learning approach for fault diagnosis, addressing domain shift and data scarcity issues in industrial settings.

Findings

Effective in multiple transfer scenarios

Improves fault diagnosis accuracy with limited labeled data

Visualizations confirm domain feature alignment

Abstract

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.