标题： KANS：多变量工业过程中的软测量知识发现图注意力网络 显示英文标题

标题： KANS: Knowledge Discovery Graph Attention Network for Soft Sensing in Multivariate Industrial Processes

摘要： 软传感技术对于难以测量的变量在工业过程中通常是至关重要的。 当前的做法严重依赖于传统的建模技术，这些技术在提高准确性方面表现出成功。 然而，它们忽视了复杂过程变量之间的非线性特性、动态特征和非欧几里得依赖关系。 为了解决这些挑战，我们提出了一种称为知识发现图注意力网络的有效软传感框架（KANS）。 与现有的深度学习软传感器模型不同，KANS可以在没有预定义拓扑结构的情况下发现多变量工业过程之间的内在相关性和不规则关系。 首先，引入了一种无监督的图结构学习方法，结合不同传感器嵌入之间的余弦相似性以捕捉传感器之间的相关性。 接下来，我们提出了基于图注意力的表示学习，可以并行计算多变量数据，以增强模型在学习复杂传感器节点和边方面的性能。 为了充分探索KANS，还进行了知识发现分析，以展示模型的可解释性。 实验结果表明，KANS在软传感性能方面显著优于所有基线和最先进的方法。 此外，分析表明，KANS可以在没有领域知识的情况下找到与不同过程变量密切相关的传感器，从而显著提高软传感的准确性。 显示英文摘要

摘要： Soft sensing of hard-to-measure variables is often crucial in industrial processes. Current practices rely heavily on conventional modeling techniques that show success in improving accuracy. However, they overlook the non-linear nature, dynamics characteristics, and non-Euclidean dependencies between complex process variables. To tackle these challenges, we present a framework known as a Knowledge discovery graph Attention Network for effective Soft sensing (KANS). Unlike the existing deep learning soft sensor models, KANS can discover the intrinsic correlations and irregular relationships between the multivariate industrial processes without a predefined topology. First, an unsupervised graph structure learning method is introduced, incorporating the cosine similarity between different sensor embedding to capture the correlations between sensors. Next, we present a graph attention-based representation learning that can compute the multivariate data parallelly to enhance the model in learning complex sensor nodes and edges. To fully explore KANS, knowledge discovery analysis has also been conducted to demonstrate the interpretability of the model. Experimental results demonstrate that KANS significantly outperforms all the baselines and state-of-the-art methods in soft sensing performance. Furthermore, the analysis shows that KANS can find sensors closely related to different process variables without domain knowledge, significantly improving soft sensing accuracy.