Deep learning-based soft sensor has been widely used for quality prediction in modern industry. Traditional deep learning like stacked autoencoder (SAE) only captures the feature representations by minimizing the global reconstruction errors, which causes a loss of the intrinsic geometric structure embedded in the raw data. To address this problem, a nonlocal and local structure preserving stacked autoencoder (NLSP-SAE) is proposed for soft sensor. Different from the original SAE, NLSP-SAE aims to extract the meaningful structure-relevant features by establishing a new objective function with a regularizer of the nonlocal and local data structure information. For local structure preserving, NLSP-SAE enforces two adjacent data points to be near each other in the reconstructed space. While for nonlocal structure preserving, NLSP-SAE constrains two nonadjacent data points to be far apart from each other. The application on an industrial hydrocracking process demonstrates that NLSP-SAE can improve the prediction accuracy for quality variables.

基于深度学习的软传感器已广泛用于现代工业中的质量预测。传统的深度学习，如堆叠自编码器 (SAE)，仅通过最小化全局重建误差来捕获特征表示，这会导致嵌入原始数据中的内在几何结构丢失。为了解决这个问题，提出了一种用于软传感器的非局部和局部结构保留堆叠自动编码器（NLSP-SAE）。与原始 SAE 不同，NLSP-SAE 旨在通过使用非局部和局部数据结构信息的正则化器建立新的目标函数来提取有意义的结构相关特征。对于局部结构保留，NLSP-SAE 强制两个相邻数据点在重建空间中彼此靠近。而对于非局部结构保持，NLSP-SAE 将两个不相邻的数据点限制为彼此远离。在工业加氢裂化过程中的应用表明，NLSP-SAE 可以提高质量变量的预测精度。