In order to ensure the long-term stable operation of a large-scale industrial process, it is necessary to detect and solve the minor abnormal conditions in time. However, the large-scale industrial process contains a large number of complex related process variables, some of which are redundant for abnormal condition detection. To solve this problem, a new decentralized PCA modeling method based on relevance and redundancy variable selection (RRVS-DPCA) is presented. First, considering the complex dynamic relation of process variables, a variable selection strategy based on relevance and redundancy (RRVS) is designed to select variables that carried the most profitable information from different temporal dimensions for each key process variables, so the optimal variable sub-block for each individual key process variables can be obtained. Then, for each sub-block, a corresponding sub-PCA monitoring model is established. The sub-blocks’ monitoring results are combined to form a probability statistical indicator through a Bayesian inference. Finally, the weighed contribution plot method is proposed to find the root cause of a fault. The proposed method is compared with several state-of-the-art process monitoring methods on a numerical example and the Tennessee Eastman benchmark process. The comparison results illustrate the feasibility and effectiveness of the proposed monitoring scheme.

为了确保大型工业过程的长期稳定运行，有必要及时发现并解决较小的异常情况。但是，大规模工业过程包含大量复杂的相关过程变量，其中一些对于异常情况检测是多余的。为了解决这个问题，提出了一种基于相关性和冗余变量选择（RRVS-DPCA）的分散式PCA建模方法。首先，考虑到过程变量之间的复杂动态关系，设计了一种基于相关性和冗余度（RRVS）的变量选择策略，以针对每个关键过程变量从不同的时间维度中选择携带最有利可图的信息的变量，因此，最优变量可以获得每个关键过程块的变量。然后，对于每个子块，建立相应的子PCA监视模型。子块的监视结果通过贝叶斯推断组合在一起，形成概率统计指标。最后，提出了加权贡献图法来寻找故障的根本原因。在数值示例和田纳西州伊士曼基准测试过程中，将所提出的方法与几种最新的过程监控方法进行了比较。比较结果说明了所提出的监测方案的可行性和有效性。在数值示例和田纳西州伊士曼基准测试过程中，将所提出的方法与几种最新的过程监控方法进行了比较。比较结果说明了所提出的监测方案的可行性和有效性。在数值示例和田纳西州伊士曼基准测试过程中，将所提出的方法与几种最新的过程监控方法进行了比较。比较结果说明了所提出的监测方案的可行性和有效性。