Abstract Since modern industrial processes contain a lot of variables and the relationships and dynamic characters among them are also complex. Hence, it is difficult to implement process monitoring by conventional methods. Aiming at the problem, a distributed dynamic slow feature analysis method with minimal redundancy maximal relevance (mRMR-DDSFA) is proposed. Firstly, the minimal redundancy maximal relevance (mRMR) is utilized to divide the most related variables into same block, which not only reduce the redundancy, but also retain the maximal correlation among variables. Then, a monitoring model based on dynamic slow feature analysis (DSFA) is established in each sub-block. In addition, Bayesian inference is carried out to integrate the detection results in each sub-block to achieve comprehensive statistical indicators, after that, the fault detection is realized for the plant-wide process. Finally, the effectiveness and superiority of the new method are verified by the simulations on the real-world diesel working process, the Tennessee Eastman (TE) platform and the continuous stirred tank reactor (CSTR) process.

中文翻译：

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基于最小冗余最大相关动态慢特征分析的分布式动态过程监控

摘要 由于现代工业过程包含大量变量，它们之间的关系和动态特征也很复杂。因此，传统方法难以实现过程监控。针对该问题，提出了一种具有最小冗余最大相关性的分布式动态慢特征分析方法(mRMR-DDSFA)。首先，利用最小冗余最大相关（mRMR）将最相关的变量划分到同一个块中，既减少了冗余，又保留了变量之间的最大相关性。然后，在每个子块中建立基于动态慢特征分析（DSFA）的监控模型。另外进行贝叶斯推理，将各个子块中的检测结果进行整合，达到综合统计指标，之后，实现全厂过程的故障检测。最后，通过对真实世界柴油工作过程、田纳西伊士曼（TE）平台和连续搅拌釜反应器（CSTR）过程的模拟，验证了新方法的有效性和优越性。