# Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis

arXiv: https://arxiv.org/abs/1903.06753 · HTML: https://mlsanity.com/paper/1903.06753/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2019.

## Built on by (most cited first, 13 of 13)

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- [Deep transfer learning for image classification: a survey](https://mlsanity.com/paper/2205.09904/) (2022)
- [Data-driven Machinery Fault Diagnosis: A Comprehensive Review](https://mlsanity.com/paper/2405.18843/) (2024)
- [Improving Semi-Supervised Learning for Remaining Useful Lifetime Estimation Through Self-Supervision](https://mlsanity.com/paper/2108.08721/) (2021)
- [EverAdapt: Continuous Adaptation for Dynamic Machine Fault Diagnosis Environments](https://mlsanity.com/paper/2407.17117/) (2024)
- [Attention-Based Multiscale Temporal Fusion Network for Uncertain-Mode Fault Diagnosis in Multimode Processes](https://mlsanity.com/paper/2504.05172/) (2025)
- [A domain adaptation neural network for digital twin-supported fault diagnosis](https://mlsanity.com/paper/2505.21046/) (2025)
- [Intelligent Icing Detection Model of Wind Turbine Blades Based on SCADA data](https://mlsanity.com/paper/2101.07914/) (2021)
- [Single-image driven 3d viewpoint training data augmentation for effective wine label recognition](https://mlsanity.com/paper/2404.08820/) (2024)
- [Mean-field limit from general mixtures of experts to quantum neural networks](https://mlsanity.com/paper/2501.14660/) (2025)
- [An Advanced Convolutional Neural Network for Bearing Fault Diagnosis under Limited Data](https://mlsanity.com/paper/2509.11053/) (2025)
- [Wasserstein distance based semi-supervised manifold learning and application to GNSS multi-path detection](https://mlsanity.com/paper/2512.05567/) (2025)

## Abstract (arXiv)

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.