As the data-driven Francis turbine units (FTUs) deterioration assessment method is widely investigated, the data quality in actual industrial scene has become an important prerequisite to restrict the method performance. However, existing data augmentation methods often lie in simply mixing real data and simulated data, ignoring the inherent cross-domain state relationships. In this paper, a data-model-interactive enhancement-based FTU health condition assessment using graph driven health benchmark model (HBM) is proposed. First, the pseudo-signals outputs of the mechanism digital twin (DT) model by calculative fluid dynamics (CFD) calculation are modified by critical working condition parameters, extending the theoretical assessment domain. To achieve data-model-interaction through cross-domain state coordination of digital and real data, a digital-reality hybrid graph is constructed from nodes mixing both actual and pseudo through node similarity. The hybrid spatial condition structure created by capture node status association explicitly enhances the sample representation ability. Avoiding excess noise effects, a knowledge-based unsupervised graph pruning regulation is adopted to refine the original graph considering noise and data reality. Finally, the spatial–temporal dependencies hidden in the refined graphs are mined by the designed hybrid graph neural network-based HBM, and output the assessed degradation label values. Verification experiments show that the proposed assessment method can effectively fit FTU deterioration under low-quality onsite data, and is more robust than the comparison methods.

中文翻译：

---

使用图驱动的健康基准模型进行基于数据模型交互式增强的混流式水轮机机组健康状况评估

随着数据驱动的混流式水轮机机组（FTU）劣化评估方法的广泛研究，实际工业场景中的数据质量已成为制约该方法性能的重要前提。然而，现有的数据增强方法往往只是简单地混合真实数据和模拟数据，忽略了固有的跨域状态关系。本文提出了一种使用图驱动健康基准模型（HBM）的基于数据模型交互式增强的 FTU 健康状况评估。首先，通过计算流体动力学（CFD）计算的机构数字孪生（DT）模型的伪信号输出通过关键工况参数进行修改，扩展了理论评估域。为了通过数字和真实数据的跨域状态协调来实现数据模型交互，通过节点相似性混合实际和伪节点来构建数字现实混合图。通过捕获节点状态关联创建的混合空间条件结构显着增强了样本表示能力。为了避免过多的噪声影响，采用基于知识的无监督图剪枝规则来考虑噪声和数据现实来细化原始图。最后，通过设计的基于混合图神经网络的 HBM 挖掘隐藏在细化图中的时空依赖性，并输出评估的退化标签值。验证实验表明，所提出的评估方法能够有效拟合低质量现场数据下的FTU恶化情况，并且比对比方法更加鲁棒。