The purpose of this article is to present and evaluate the performance of a new procedure for industrial process diagnosis. This method is based on the use of a bayesian network as a classifier. But, as the classification performances are not very efficient in the space described by all variables of the process, an identification of important variables is made. This feature selection is made by...

An improved mixture of probabilistic principal component analysis (PPCA) has been introduced for nonlinear datadriven process monitoring in this paper. To realize this purpose, the technique of a mixture of probabilistic principal component analysers is utilized to establish the model of the underlying nonlinear process with local PPCA models, where a novel composite monitoring statistic is pro...

Dynamic Time Warping (DTW) can be used to minimize distance between two sequences which display the same trends but are not perfectly aligned with each other. This feature makes DTW an effective pattern classification method for sequence matching. In Ref. [1], a fault diagnosis method using pattern classification based on adaptive rank-order morphological transform has been proposed. It uses Eu...

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 ...

Projection to latent structures (PLS)model has beenwidely used in quality-related processmonitoring, as it can establish amapping relationship between process variables and quality index variables. To enhance the adaptivity of PLS, kernel PLS (KPLS) as an advanced version has been proposed for nonlinear processes. In this paper, we discuss a new total kernel PLS (T-KPLS) for nonlinear quality-r...

Statistics pattern analysis (SPA) is a new multivariate statistical monitoring framework proposed by the authors recently. It addresses some challenges that cannot be readily addressed by the commonly used multivariate statistical methods such as principal component analysis (PCA) in monitoring batch processes in the semiconductor industry. It was later extended to the monitoring of continuous ...

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...

Ž . Principal component analysis PCA is the most commonly used dimensionality reduction technique for detecting and diagnosing faults in chemical processes. Although PCA contains certain optimality properties in terms of fault detection, and Ž . has been widely applied for fault diagnosis, it is not best suited for fault diagnosis. Discriminant partial least squares DPLS has been shown to impro...