The measured data of the large-scale industrial process usually has shown the nonstationary, non-Gaussian, dynamic characteristics, however, most traditional methods did not consider the multiple characteristics coexistence and viewed all the variables as a whole. To make up the deficiencies of the conventional methods, this paper proposes a novel reorganized multiple characteristics subspaces integrated with Bayesian inference (RMS-BI) monitoring strategy for large-scale dynamic process. Firstly, the overall process variables are divided into three subspaces by Jarque–Bera (J–B) test and Augmented Dickey–Fuller (ADF) test, which are the nonstationary subspace, stationary Gaussian subspace, and stationary non-Gaussian subspace. Then, the cointegration analysis (CA), dynamic principal component analysis (DPCA) and dynamic independent component analysis (DICA) models are singled out to monitor the abnormities in the three subspaces, respectively. After that, the monitoring results of the multiple subspaces are integrated by Bayesian inference (BI) to obtain global monitoring statistics. Finally, case studies on the Tennessee Eastman process and the real-world diesel working process are used to demonstrate the availability of the RMS-BI method.

大规模工业过程的测量数据通常显示出非平稳，非高斯动态特征，但是，大多数传统方法并未考虑多重特征并存，而是将所有变量视为一个整体。为了弥补传统方法的不足，本文提出了一种与贝叶斯推理（RMS-BI）集成的大规模动态过程集成的新颖的重组多特征子空间。首先，通过Jarque-Bera（J-B）检验和增强Dickey-Fuller（ADF）检验将整个过程变量分为三个子空间，它们是非平稳子空间，平稳高斯子空间和平稳非高斯子空间。然后，进行协整分析（CA），动态主成分分析（DPCA）和动态独立成分分析（DICA）模型被选出来分别监视三个子空间中的异常。之后，利用贝叶斯推断（BI）集成多个子空间的监视结果，以获得全局监视统计信息。最后，通过田纳西伊士曼过程和实际柴油工作过程的案例研究，证明了RMS-BI方法的可用性。