Principal component analysis (PCA) and Partial least square (PLS) are powerful multivariate statistical tools that have been successfully applied for process monitoring. They are efficient in dimension reduction and are suitable for processing large amount of data. Nevertheless, their application scope is restricted to static processes where the dynamics are ignored. In order to achieve improve...

Transfer entropy (TE) is a model-free approach based on information theory to capture causality between variables, which has been used for the modeling and monitoring of, and fault diagnosis in, complex industrial processes. It is able to detect the causality between variables without assuming any underlying model, but it is computationally burdensome. To overcome this limitation, a hybrid meth...

Complex multimode processes may have dynamic operation scenario shifts and strong transient behaviors so that the conventional monitoring methods become ill-suited. In this article, a new particle filter based dynamic Gaussian mixture model (DGMM) is developed by adopting particle filter resampling method to update the mixture model parameters in a dynamic fashion. Then the particle filtered Ba...

Chemical processes usually exhibit complex, high-dimensional and non-Gaussian characteristics, the diagnosis of faults in chemical is particularly important. To address this problem, paper proposes a novel fault method based on Bernoulli shift coyote optimization algorithm (BCOA) to optimize kernel extreme learning machine classifier (KELM). Firstly, random forest treebagger (RFtb) used select ...

Clustering algorithms and deep learning methods have been widely applied in the multimode process monitoring. However, for data with unknown mode, traditional clustering can hardly identify number of modes automatically. Further, learn effective features from nonlinear data, while extracted cannot follow Gaussian distribution, which may lead to incorrect control limit fault detection. In this p...

The problem of integrated process and control system design is discussed in this paper. We formulate the optimization problem as a mixed-integer nonlinear programming problem subject to differential-algebraic constraints. This class of problems is frequently non-convex and local optimization techniques usually fail to locate the global solution. Thus, we propose a global optimization algorithm ...

Multivariate statistical process control (MSPC) has been successfully applied to chemical processes. In order to improve the performance of fault detection, two kinds of advanced methods, known as moving principal component analysis (MPCA) and DISSIM, have been proposed. In MPCA and DISSIM, an abnormal operation can be detected by monitoring the directions of principal components (PCs) and the ...

A diagnostic algorithm is described in this article that is based on clustering qualitative event sequences called traces. A sufficient number of training traces are used instead of an internal model to specify the faulty models of the system. The diagnosis consists of two phases. In the off-line training phase diagnostic clusters representing nominal and faulty behavior are formed from the set...