Abstract Deep learning has recently caught much attention in the industrial processes, particularly for soft sensor applications. However, most traditional deep learning networks cannot extract local features for data modeling. To overcome this problem, a novel stacked locality preserving autoencoder (S-LPAE) is proposed in this paper. First, the neighborhood topological structure is built for the historical samples and the weights between the neighbor samples are calculated. Then, locality preserving autoencoder (LPAE) is designed to minimize both the reconstruction error and the additional local preserving constraint of the training dataset, with which the potential features can better preserve the local data structure. After that, multiple LPAE modules are sequentially stacked to construct the S-LPAE network to obtain deep locality-preserving features. Finally, the extracted features are directly used for the output prediction of soft sensor. To validate the performance of the proposed algorithm, it is applied to an industrial hydrocracking process to predict the 90% boiling point of aviation kerosene and the 50% boiling point of diesel.

中文翻译：

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用于特征提取的堆叠局部保留自动编码器及其在工业过程数据建模中的应用

摘要 深度学习最近在工业过程中引起了很多关注，特别是对于软传感器应用。然而，大多数传统的深度学习网络无法提取局部特征进行数据建模。为了克服这个问题，本文提出了一种新颖的堆叠局部保持自动编码器（S-LPAE）。首先，对历史样本建立邻域拓扑结构，计算邻域样本之间的权重。然后，局部保留自动编码器（LPAE）旨在最小化训练数据集的重建误差和额外的局部保留约束，从而使潜在特征可以更好地保留局部数据结构。之后，多个 LPAE 模块依次堆叠以构建 S-LPAE 网络，以获得深度保持局部性的特征。最后，提取的特征直接用于软传感器的输出预测。为了验证所提算法的性能，将其应用于工业加氢裂化过程以预测航空煤油的 90% 沸点和柴油的 50% 沸点。