# Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application

arXiv: https://arxiv.org/abs/1804.07265 · HTML: https://mlsanity.com/paper/1804.07265/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2018.

## Built on by (most cited first, 9 of 9)

- [ChatGPT-Like Large-Scale Foundation Models for Prognostics and Health Management: A Survey and Roadmaps](https://mlsanity.com/paper/2305.06472/) (2023)
- [An Uncertainty-Informed Framework for Trustworthy Fault Diagnosis in Safety-Critical Applications](https://mlsanity.com/paper/2111.00874/) (2021)
- [Stylized Face Sketch Extraction via Generative Prior with Limited Data](https://mlsanity.com/paper/2403.11263/) (2024)
- [Physics-Informed Deep Learning: A Promising Technique for System Reliability Assessment](https://mlsanity.com/paper/2108.10828/) (2021)
- [A Novel Deep Parallel Time-series Relation Network for Fault Diagnosis](https://mlsanity.com/paper/2112.03405/) (2021)
- [Technical Language Supervision for Intelligent Fault Diagnosis in Process Industry](https://mlsanity.com/paper/2112.07356/) (2021)
- [A platform for causal knowledge representation and inference in industrial fault diagnosis based on cubic DUCG](https://mlsanity.com/paper/2203.12802/) (2022)
- [Analysis and Applications of Deep Learning with Finite Samples in Full Life-Cycle Intelligence of Nuclear Power Generation](https://mlsanity.com/paper/2311.04247/) (2023)
- [Neighbor displacement-based enhanced synthetic oversampling for multiclass imbalanced data](https://mlsanity.com/paper/2501.04099/) (2025)

## Abstract (arXiv)

In recent years, an increasing popularity of deep learning model for intelligent condition monitoring and diagnosis as well as prognostics used for mechanical systems and structures has been observed. In the previous studies, however, a major assumption accepted by default, is that the training and testing data are taking from same feature distribution. Unfortunately, this assumption is mostly invalid in real application, resulting in a certain lack of applicability for the traditional diagnosis approaches. Inspired by the idea of transfer learning that leverages the knowledge learnt from rich labeled data in source domain to facilitate diagnosing a new but similar target task, a new intelligent fault diagnosis framework, i.e., deep transfer network (DTN), which generalizes deep learning model to domain adaptation scenario, is proposed in this paper. By extending the marginal distribution adaptation (MDA) to joint distribution adaptation (JDA), the proposed framework can exploit the discrimination structures associated with the labeled data in source domain to adapt the conditional distribution of unlabeled target data, and thus guarantee a more accurate distribution matching. Extensive empirical evaluations on three fault datasets validate the applicability and practicability of DTN, while achieving many state-of-the-art transfer results in terms of diverse operating conditions, fault severities and fault types.