用数字孪生减少故障诊断对真实故障数据的依赖
Use Digital Twins to Support Fault Diagnosis From System-level Condition-monitoring Data
- 借助数字孪生生成仿真数据，降低真实故障数据需求
- 仅用4个电机的数据即可识别9种故障位置与模式
- 适合缺乏故障样本的工业系统故障检测场景
深度学习模型为数据驱动的故障诊断带来了巨大机遇，但需要大量带标签的故障数据进行训练。本文提出利用数字孪生支持构建数据驱动的故障诊断模型，以减少训练过程中对故障数据的依赖。所开发的诊断模型能够基于系统级状态监测数据识别组件级故障。该框架在真实机器人系统上进行了评估，结果表明，由数字孪生训练的深度学习模型能够诊断出来自4个不同电机的9种故障/失效的位置与模式。然而，当数字孪生模型与真实系统存在偏差时，模型性能仍有提升空间。
原文摘要 · Abstract (English)
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.
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