In modern industrial processes, each subsystem interacts frequently and involves a large number of process variables with complex relations, which challenge process monitoring. In this paper, a distributed process monitoring method based on joint mutual information (JMI) and projective dictionary pair learning (DPL) is proposed for effective process monitoring in industrial systems with multimode, complex, and high-dimensional data. Firstly, considering the interactive information, redundancy and irrelevance among process variables, an automatic block division method based on JMI is proposed to divide process variables into several low dimensional blocks. Secondly, DPL-based monitoring model is established in each block of each mode. According to the multimode characteristic of industrial processes, a joint probability based on reconstruction error is proposed for mode recognition. Then, Bayesian inference method that fuses block statistics into global statistics is introduced for anomaly detection. The anomaly source is further determined by defining the block contribution coefficient and variable contribution coefficient. Finally, the effectiveness of the proposed method is demonstrated by a numerical simulation, Tennessee Eastman benchmark test, and experiments in an aluminum electrolysis industrial process.

在现代工业过程中，各个子系统交互频繁，涉及大量关系复杂的过程变量，对过程监控提出了挑战。本文提出了一种基于联合互信息（JMI）和投影字典对学习（DPL）的分布式过程监控方法，用于多模式、复杂和高维数据的工业系统中的有效过程监控。首先，考虑过程变量之间的交互信息、冗余和不相关性，提出了一种基于JMI的自动块划分方法，将过程变量划分为若干个低维块。其次，在每种模式的每个块中建立基于DPL的监控模型。根据工业过程的多模特性，提出了一种基于重构误差的联合概率进行模式识别。然后，将块统计融合到全局统计中的贝叶斯推理方法被引入用于异常检测。通过定义块贡献系数和可变贡献系数进一步确定异常源。最后，通过数值模拟、田纳西伊士曼基准测试和铝电解工业过程实验证明了所提出方法的有效性。