Industrial process data are closely related to production conditions and are essentially complex time series with high nonlinearity and dynamics. To solve the challenge of insufficient feature extraction of industrial process data, resulting in poor real-time monitoring of key quality variables, we propose an interpretable industrial soft sensor based on Graph Sampling and Aggregation Temporal Convolutional Network Improved by Multi-head Self-Attention (GraphSAGE-IMATCN) for predicting the trend of key quality variables in real time. Firstly, a three-dimensional data development strategy for batch processing is designed, and the maximum information coefficient (MIC) is introduced, and the threshold function is established by combining kernel density estimation to extract the characteristic variables with high quality correlation, and the explanatory and reliability of the model are enhanced by statistical methods. Secondly, a deep graph sampling aggregation (GraphSAGE) structure is designed for industrial big data, which aggregated features based on adjacent nodes and captured the context information of key nodes and serialized the extracted features to improve the computing speed of the model by combining the parallel computing advantages of the time convolutional network. Then, to overcome the data of different batch sizes and production scales, the residual structure of the Temporal Convolutional Network (TCN) is optimized by using Filter Response Normalization (FRN) to enhance the generalization and robustness of the model. Then, the multi-head self-attention mechanism (MHSA) is introduced to enhance the parallelism of the model, and the inference speed of the model is optimized to meet the key requirements of real-time performance for industrial process monitoring. Finally, the effectiveness of the proposed model is verified through experiments on the penicillin fermentation process and the debutanizer column.

中文翻译：

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基于 GraphSAGE-IMATCN 的非线性动态工业过程软传感器模型


工业过程数据与生产条件密切相关，本质上是具有高非线性和动态性的复杂时间序列。针对工业过程数据特征提取不足导致关键质量变量实时监测不佳的挑战，我们提出了一种基于多头自注意力改进的图采样和聚合时间卷积网络 （GraphSAGE-IMATCN） 的可解释工业软传感器，用于实时预测关键质量变量的趋势。首先，设计了面向批处理的三维数据开发策略，并引入最大信息系数（MIC），通过结合核密度估计建立阈值函数，提取具有高质量相关性的特征变量，并通过统计方法增强了模型的解释性和可靠性。其次，针对工业大数据设计了深度图采样聚合（GraphSAGE）结构，结合时间卷积网络的并行计算优势，基于相邻节点聚合特征并捕获关键节点的上下文信息并对提取的特征进行序列化，以提高模型的计算速度。然后，为了克服不同批量大小和生产规模的数据，利用滤波器响应归一化 （FRN） 优化时间卷积网络 （TCN） 的残差结构，以增强模型的泛化性和鲁棒性。然后，引入多头自注意力机制 （MHSA） 以增强模型的并行性，并优化模型的推理速度，以满足工业过程监控实时性能的关键要求。 最后，通过在青霉素发酵过程和脱酰胺柱上的实验验证了所提模型的有效性。