# Use Digital Twins to Support Fault Diagnosis From System-level Condition-monitoring Data

arXiv: https://arxiv.org/abs/2411.01360 · HTML: https://mlsanity.com/paper/2411.01360/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 2 Nov 2024.

## Builds on (most cited first, 2 of 2)

- [A Survey of Predictive Maintenance: Systems, Purposes and Approaches](https://mlsanity.com/paper/1912.07383/) (2019)
- [Robot Kinematics: Motion, Kinematics and Dynamics](https://mlsanity.com/paper/2211.15093/) (2022)

## Built on by (most cited first, 2 of 2)

- [A domain adaptation neural network for digital twin-supported fault diagnosis](https://mlsanity.com/paper/2505.21046/) (2025)
- [Digital Twin and Agentic AI for Wild Fire Disaster Management: Intelligent Virtual Situation Room](https://mlsanity.com/paper/2602.08949/) (2026)

## Abstract (arXiv)

Deep learning models have created great opportunities for data-driven fault diagnosis but they require large amount of labeled failure data for training. In this paper, we propose to use a digital twin to support developing data-driven fault diagnosis model to reduce the amount of failure data used in the training process. The developed fault diagnosis models are also able to diagnose component-level failures based on system-level condition-monitoring data. The proposed framework is evaluated on a real-world robot system. The results showed that the deep learning model trained by digital twins is able to diagnose the locations and modes of 9 faults/failure from 4 different motors. However, the performance of the model trained by a digital twin can still be improved, especially when the digital twin model has some discrepancy with the real system.