用域对抗网络缩小仿真与真实故障诊断的差距
A domain adaptation neural network for digital twin-supported fault diagnosis
- 引入域对抗神经网络，实现仿真数据到真实数据的知识迁移
- 在真实数据上将CNN准确率从70.00%提升至80.22%
- 适合需要跨仿真与真实场景部署的工业故障诊断系统
数字孪生通过生成仿真数据为基于深度学习的故障诊断提供解决方案，但仿真与真实系统间的差异会导致模型在实际应用中性能显著下降。为此，本文提出一种基于域对抗神经网络（DANN）的故障诊断框架，实现从仿真（源域）到真实世界（目标域）的数据知识迁移。采用公开的机器人故障诊断数据集进行评估，包含3,600条由数字孪生生成的序列和90条来自物理系统的实测序列。将DANN与CNN、TCN、Transformer、LSTM等轻量级深度学习模型对比，实验表明引入域适应显著提升诊断性能。例如，将DANN应用于基线CNN模型，在真实测试数据上准确率从70.00%提升至80.22%，验证了域适应在弥合仿真到真实差距方面的有效性。
原文摘要 · Abstract (English)
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.
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