As a typical process monitoring method for the large‐scale industrial process, the distributed principal components analysis (DPCA) needs to be improved because of its rough selection for the variables in each subblock. Moreover, for DPCA, the process dynamic property is ignored and invalid fault diagnosis may occur. Therefore, a performance‐driven distributed canonical variate analysis (DCVA) is proposed. Firstly, with historical fault information, the genetic algorithm is utilized to select appropriate variables for each subblock; secondly, canonical variate analysis is introduced to capture the dynamic information for performance improvement; finally, a novel fault diagnosis method is developed for the DCVA model. Case studies on a numerical example and the Tennessee Eastman benchmark process demonstrate the effectiveness of the proposed model.

中文翻译：

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基于性能驱动的分布式规范变量分析的大规模动态过程监控

作为大规模工业过程的典型过程监控方法，分布式主成分分析（DPCA）由于对每个子块中的变量选择粗略，需要改进。此外，对于DPCA，过程动态属性被忽略，可能会出现无效的故障诊断。因此，提出了一种性能驱动的分布式规范变量分析（DCVA）。首先，根据历史故障信息，利用遗传算法为每个子块选择合适的变量；其次，引入规范变量分析来捕获动态信息以提高性能；最后，针对 DCVA 模型开发了一种新的故障诊断方法。数值示例和田纳西伊士曼基准过程的案例研究证明了所提出模型的有效性。