Nonlinear process modeling is a primary task in intelligent manufacturing, aiming at extracting high-value features from massive process data for further process analysis like process monitoring. However, it is still a challenge to develop nonlinear process models with robust representation capability for diverse process faults. From the new perspective of the correlation between process variables, this paper develops a nonlinear process modeling algorithm to adaptively preserve the features of both global and local inter-variable structures, in order to fully exploit inter-variable features for enhancing the nonlinear representation of process operating conditions. Specifically, a unidimensional convolutional operation with a self-attention mechanism is proposed to simultaneously extract global and local inter-variable structures, wherein different attentions can be adaptively adjusted to these two structures for the final aggregation of them. Besides, cooperating with a two-dimensional dynamic data extension, the unidimensional convolutional operation can represent the overall temporal relationship between process samples. Through stacking a collection of these convolutional operations, a ResNet-style convolutional neural network then is constructed to extract high-order nonlinear features. Experiments on the Tennessee Eastman process validate the effectiveness of the proposed algorithm for two vital process monitoring problems—fault detection and fault identification.

非线性过程建模是智能制造中的一项主要任务，旨在从海量过程数据中提取高价值特征，以进行进一步的过程分析，如过程监控。然而，为各种过程故障开发具有鲁棒表示能力的非线性过程模型仍然是一个挑战。从过程变量之间相关性的新视角出发，提出一种非线性过程建模算法，自适应地保留全局和局部变量间结构的特征，以充分利用变量间特征增强过程的非线性表示。运行条件。具体来说，提出了一种具有自注意力机制的一维卷积运算，以同时提取全局和局部变量间结构，其中，不同的注意力可以自适应地调整到这两个结构上，以便最终聚合它们。此外，配合二维动态数据扩展，一维卷积运算可以表示过程样本之间的整体时间关系。通过堆叠这些卷积操作的集合，然后构建一个 ResNet 风格的卷积神经网络来提取高阶非线性特征。田纳西伊士曼过程的实验验证了所提出的算法对两个重要过程监控问题的有效性——故障检测和故障识别。一维卷积运算可以表示过程样本之间的整体时间关系。通过堆叠这些卷积操作的集合，然后构建一个 ResNet 风格的卷积神经网络来提取高阶非线性特征。田纳西伊士曼过程的实验验证了所提出的算法对两个重要过程监控问题的有效性——故障检测和故障识别。一维卷积运算可以表示过程样本之间的整体时间关系。通过堆叠这些卷积操作的集合，然后构建一个 ResNet 风格的卷积神经网络来提取高阶非线性特征。田纳西伊士曼过程的实验验证了所提出的算法对两个重要过程监控问题的有效性——故障检测和故障识别。