Zhang Fengzhen, Jin Qibing, Li Dazi, Zhang Yang, Zhu Qian
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China.
ACS Omega. 2024 Feb 16;9(8):9486-9502. doi: 10.1021/acsomega.3c09122. eCollection 2024 Feb 27.
The rapid development of big data technology and machine learning has increasingly focused attention on fault diagnosis in complex chemical processes. However, data-driven approaches often overlook the inherent physical correlations within the system and lack a robust mechanism for providing trusted explanations for fault diagnosis. To address this challenge, a graph-based fault diagnosis model framework is proposed along with a dependable fault node diagnosis analysis method. In order to enhance the extraction of chemical process features from a spatial perspective, a graph convolution network (GCN)-based node spatial encoding module is integrated. The construction of the adjacency matrix involves combining a priori knowledge of chemical processes with Pearson correlation, thereby incorporating the physical correlations between nodes. Simultaneously, to capture temporal dependencies in fault data, a spatiotemporal feature fusion module based on the long short-term memory network (LSTM) is employed. In terms of model training, a dual-supervision strategy is adopted to ensure stable convergence of the multiclass fault diagnosis model. For model inference, a multi-model voting strategy is designed to mitigate accuracy degradation resulting from model prediction bias. To tackle the interpretability challenge, a fault diagnosis analysis method based on node masking is designed, effectively identifying critical nodes contributing to system faults. Experimental validation on the Tennessee Eastman process demonstrates the effectiveness of the proposed model, achieving high accuracy in fault diagnosis. The average fault diagnosis rate for all fault types reaches 0.9844, showcasing state-of-the-art performance in fault diagnosis.
大数据技术和机器学习的快速发展，使得复杂化学过程中的故障诊断越来越受到关注。然而，数据驱动的方法往往忽略了系统内部固有的物理相关性，并且缺乏为故障诊断提供可靠解释的强大机制。为应对这一挑战，提出了一种基于图的故障诊断模型框架以及一种可靠的故障节点诊断分析方法。为了从空间角度增强化学过程特征的提取，集成了基于图卷积网络（GCN）的节点空间编码模块。邻接矩阵的构建涉及将化学过程的先验知识与皮尔逊相关性相结合，从而纳入节点之间的物理相关性。同时，为了捕捉故障数据中的时间依赖性，采用了基于长短期记忆网络（LSTM）的时空特征融合模块。在模型训练方面，采用双监督策略以确保多类故障诊断模型的稳定收敛。对于模型推理，设计了多模型投票策略以减轻模型预测偏差导致的准确率下降。为解决可解释性挑战，设计了一种基于节点掩码的故障诊断分析方法，有效识别导致系统故障的关键节点。在田纳西伊士曼过程上的实验验证证明了所提模型的有效性，在故障诊断中实现了高精度。所有故障类型的平均故障诊断率达到0.9844，展现了故障诊断方面的先进性能。
