针对汽水分离再热系统等复杂工业系统，为解决传统故障诊断模型准确率受限于故障样本稀缺和故障数据时间维度与变量维度耦合的问题，提出一种基于深度学习的故障诊断方法。首先，构建汽水分离再热数字孪生系统，用以建立故障诊断数据仓库，解决数据样本层面稀缺性的问题。其次，进一步构建基于深度残差网络的故障诊断模型，用以诊断汽水分离再热系统典型故障，包括流量不均、破口、传热恶化和阀门特性变化，从而解决数据变量层面时变、多维度的问题。结果表明：数字孪生系统能够实现汽水分离再热系统稳态、动态和故障工况的精确仿真，满足后续深度学习模型的数据需求；基于深度残差网络的故障诊断模型能够实现时变、多维工业数据的故障诊断；采用T分布随机邻域嵌入(TSNE)方法对模型可视化，可对不同故障类型的数据进行明显区分。

In order to solve the problem that the accuracy of traditional fault diagnosis model is limited by the scarcity of fault samples and the coupling of time dimension and variable dimension of fault data, a fault diagnosis method based on deep learning is proposed for complex industrial systems such as moisture separator and reheater system. Firstly, the digital twin system of moisture separator and reheater is constructed to establish the fault diagnosis data warehouse and solve the problem of scarcity of data samples. Secondly, based on the previous step, a fault diagnosis model based on deep residual network is constructed to diagnose the typical faults of steam water separation and reheat system, including uneven flow, break, deterioration of heat transfer and change of valve characteristics, so as to solve the problem of timevarying and multidimensional data variables. The simulation results show that the digital twin system can realize the accurate simulation of the steadystate, dynamic and fault conditions of the steam water separation and reheat system, and meet the data requirements of the subsequent indepth learning model; the fault diagnosis model based on deep residual network can realize the fault diagnosis of timevarying and multidimensional industrial data. The Tdistributed stochastic neighborhood embedding (TSNE) methodis used to visualize the model and verify that the suggested diagnostic model distinguishes significantly between different fault types of data.

TL48

核反应堆系统设计技术重点实验室资助项目(HT-WDZC-02-20020007)