标题： 基于软传感器的工业过程建模与监测的可解释时空特征提取 显示英文标题

标题： Interpretable Spatio-Temporal Features Extraction based Industrial Process Modeling and Monitoring by Soft Sensor

摘要： 数据驱动的软测量技术已在复杂工业过程中得到广泛应用。然而，由软测量提取可解释的空间-时间特征仍是一项挑战。基于此，本文提出了一种新颖的方法，称为时空一致且可解释的模型（STCIM）。首先，通过远距离拓扑时空一致性提取块捕获并对齐时间和空间特征。然后，通过明确赋予潜在变量物理意义，将这些特征映射到可解释的潜在空间以进一步预测。所提出的STCIM的有效性通过两个生成的数据集和一个燃煤电厂的实际数据集建模进行了验证。相应的实验表明：1）STCIM的泛化能力优于其他方法，尤其是在不同的操作情况下；2）远距离拓扑时空一致性对于特征对齐至关重要；3）物理学引导的可解释潜在空间损失的超参数决定了STCIM的性能。 显示英文摘要

摘要： Data-driven soft sensors have been widely applied in complex industrial processes. However, the interpretable spatio-temporal features extraction by soft sensors remains a challenge. In this light, this work introduces a novel method termed spatio-temporal consistent and interpretable model (STCIM). First, temporal and spatial features are captured and aligned by a far topological spatio-temporal consistency extraction block. Then, the features are mapped into an interpretable latent space for further prediction by explicitly giving physical meanings to latent variables. The efficacy of the proposed STCIM is demonstrated through the modeling of two generated datasets and a real-life dataset of coal-fired power plants. The corresponding experiments show: 1) The generalization of STCIM outperforms other methods, especially in different operation situations. 2) The far topological spatio-temporal consistency is vital for feature alignment. 3) The hyper-parameters of physics-informed interpretable latent space loss decide the performance of STCIM.