Abstract Hierarchical local nonlinear dynamic feature learning is of great importance for soft sensor modeling in process industry. Convolutional neural network (CNN) is an excellent local feature extractor that is suitable for process data representation. In this paper, a dynamic CNN (DCNN) strategy is designed to learn hierarchical local nonlinear dynamic features for soft sensor modeling. In DCNN, each 1D process sample is dynamically augmented into 2D data sample with lagged unlabeled process variables, which contains both spatial cross-correlations and temporal auto-correlations. Then, the convolutional and pooling layers are alternately utilized to extract the local nonlinear spatial–temporal feature from the 2D sample data matrix. Moreover, the principle is analyzed for DCNN on how it can learn the local nonlinear spatial–temporal feature from the network. The effectiveness of proposed DCNN is verified on an industrial hydrocracking process.

中文翻译：

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用于工业过程数据软传感器建模中非线性动态特征学习的动态 CNN

摘要 分层局部非线性动态特征学习对于过程工业中的软传感器建模具有重要意义。卷积神经网络（CNN）是一种优秀的局部特征提取器，适用于过程数据表示。在本文中，动态 CNN (DCNN) 策略旨在学习用于软传感器建模的分层局部非线性动态特征。在 DCNN 中，每个一维过程样本都被动态地扩充为具有滞后未标记过程变量的二维数据样本，其中包含空间互相关和时间自相关。然后，交替使用卷积层和池化层从二维样本数据矩阵中提取局部非线性时空特征。而且，分析了 DCNN 如何从网络中学习局部非线性时空特征的原理。提出的 DCNN 的有效性在工业加氢裂化过程中得到验证。