In industrial sectors such as shipping, chemical processing, and energy production, centrifugal pumps often experience failures due to harsh operational environments, making it challenging to accurately identify fault types. Traditional fault diagnosis methods, which heavily rely on existing fault datasets, suffer from limited generalization capabilities, especially when substantial labeled and specific fault sample data are lacking. This paper proposes a novel fault diagnosis approach for centrifugal pumps, utilizing a digital twin (DT) framework powered by a graph transfer learning model to address this issue. Firstly, a high-fidelity DT model is constructed to simulate the flow-induced vibration response of the impeller under different health states to enrich the type and scale of the dataset. Secondly, a graph convolutional neural networks (GCN) model is constructed to learn the knowledge of simulation data, and the Wasserstein distance between simulation data and measured data is optimized for adversarial domain adaptation, thereby achieving efficient cross-domain fault diagnosis. Experimental results demonstrate that the proposed algorithm delivers effective fault diagnosis with minimal prior knowledge and outperforms comparable models. Furthermore, the DT system developed using the proposed model enhances the operational reliability of centrifugal pumps, reduces maintenance costs, and presents an innovative application of DT technology in industrial fault diagnosis.

中文翻译：

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基于图卷积神经网络的迁移学习驱动的离心泵故障诊断数字孪生系统


在航运、化学加工和能源生产等工业领域，离心泵经常因恶劣的运行环境而发生故障，这使得准确识别故障类型具有挑战性。传统的故障诊断方法严重依赖现有的故障数据集，泛化能力有限，尤其是在缺乏大量标记的、特定的故障样本数据时。本文提出了一种新颖的离心泵故障诊断方法，利用由图迁移学习模型支持的数字孪生（DT）框架来解决这个问题。首先，构建高保真DT模型来模拟不同健康状态下叶轮的流激振动响应，丰富数据集的类型和规模。其次，构建图卷积神经网络（GCN）模型来学习仿真数据的知识，并优化仿真数据与测量数据之间的Wasserstein距离以进行对抗域适应，从而实现高效的跨域故障诊断。实验结果表明，所提出的算法可以用最少的先验知识提供有效的故障诊断，并且优于同类模型。此外，使用该模型开发的DT系统提高了离心泵的运行可靠性，降低了维护成本，并提出了DT技术在工业故障诊断中的创新应用。