Han Shiqian, Wang Pingping, Zhang Cheng, Wang Jun
College of Science, Shenyang University of Chemical Technology, Shenyang, Liaoning 110142, China.
Key Laboratory for Chemical Process IndustryIntelligent Technology of Liaoning Province, Shenyang, Liaoning 110142, China.
ACS Omega. 2025 Feb 24;10(9):8859-8869. doi: 10.1021/acsomega.4c03757. eCollection 2025 Mar 11.
Aiming at the challenge of distinguishing the contributions of variables in dynamic chemical process data, this paper proposes a novel fault diagnosis method based on the IResNet-GRU model. First, we utilize principal component analysis to compute a correlation matrix, which serves as the input for an attention module. This approach enables the evaluation of feature contributions to predictions, thereby identifying the root-cause variables responsible for faults. Concurrently, we enhance the residual network (ResNet) with the attention module to assign weights to the extracted features. The improved ResNet (IResNet) can differentiate the significance of the monitored variables. Second, we augment the raw data into two-dimensional data using sliding window technology, capturing spatial and temporal data features. Finally, a gated recurrent unit is integrated to extract dynamic features from the augmented two-dimensional data effectively. The proposed method is validated using the Tennessee-Eastman chemical process. The diagnosis results demonstrate that the proposed method outperforms conventional methods.
针对动态化工过程数据中区分变量贡献的挑战，本文提出了一种基于IResNet-GRU模型的新型故障诊断方法。首先，我们利用主成分分析来计算一个相关矩阵，该矩阵作为注意力模块的输入。这种方法能够评估特征对预测的贡献，从而识别出导致故障的根源变量。同时，我们用注意力模块增强残差网络（ResNet），以便为提取的特征分配权重。改进后的ResNet（IResNet）能够区分监测变量的重要性。其次，我们使用滑动窗口技术将原始数据扩充为二维数据，捕捉空间和时间数据特征。最后，集成门控循环单元以有效地从扩充后的二维数据中提取动态特征。所提出的方法通过田纳西-伊斯曼化工过程进行了验证。诊断结果表明，所提出的方法优于传统方法。
