# A domain adaptation neural network for digital twin-supported fault diagnosis

arXiv: https://arxiv.org/abs/2505.21046 · HTML: https://mlsanity.com/paper/2505.21046/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 27 May 2025.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [A Survey of Predictive Maintenance: Systems, Purposes and Approaches](https://mlsanity.com/paper/1912.07383/) (2019)
- [Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis](https://mlsanity.com/paper/1903.06753/) (2019)
- [Use Digital Twins to Support Fault Diagnosis From System-level Condition-monitoring Data](https://mlsanity.com/paper/2411.01360/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Hybrid Vision-Language Architecture for Automated Defect Reasoning and Report Generation in Industrial Inspection](https://mlsanity.com/paper/2605.26533/) (2026)

## Abstract (arXiv)

Digital twins offer a promising solution to the lack of sufficient labeled data in deep learning-based fault diagnosis by generating simulated data for model training. However, discrepancies between simulation and real-world systems can lead to a significant drop in performance when models are applied in real scenarios. To address this issue, we propose a fault diagnosis framework based on Domain-Adversarial Neural Networks (DANN), which enables knowledge transfer from simulated (source domain) to real-world (target domain) data. We evaluate the proposed framework using a publicly available robotics fault diagnosis dataset, which includes 3,600 sequences generated by a digital twin model and 90 real sequences collected from physical systems. The DANN method is compared with commonly used lightweight deep learning models such as CNN, TCN, Transformer, and LSTM. Experimental results show that incorporating domain adaptation significantly improves the diagnostic performance. For example, applying DANN to a baseline CNN model improves its accuracy from 70.00% to 80.22% on real-world test data, demonstrating the effectiveness of domain adaptation in bridging the sim-to-real gap.