Comparison of multivariate statistical process monitoring methods with applications to the Eastman challenge problem

Kano, Manabu; Nagao, Koji; Hasebe, Shinji; Hashimoto, Iori; Ohno, Hiromu; Strauss, Ramon; Bakshi, Bhavik R.

doi:10.1016/s0098-1354(01)00738-4

Cited by 176 publications

(109 citation statements)

References 26 publications

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“…A baseline control system was reported by [24] and the simulation has been widely used for demonstration of advanced control schemes (e.g. [23,30,22,20,40]), and for testing of fault detection and diagnosis schemes, both data driven and model-based [19,12,5,[14][15][16]34]. The original code was written in Fortran, while [29] has made an implementation in Simulink available to other researchers.…”

Section: First Principles Modelsmentioning

confidence: 99%

A continuous stirred tank heater simulation model with applications

Thornhill

Patwardhan

Shah

2008

Journal of Process Control

193

109

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This article presents a first principles simulation of a continuous stirred tank heater pilot plant at the University of Alberta. The model has heat and volumetric balances, and a very realistic feature is that instrument, actuator and process non-linearities have been carefully measured, for instance to take account of the volume occupied by heating coils in the tank. Experimental data from step testing and recordings of real disturbances are presented. The model in Simulink and the experimental data are available electronically, and some suggestions are given for their application in education, system identification, fault detection and diagnosis.

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Section: First Principles Modelsmentioning

confidence: 99%

A continuous stirred tank heater simulation model with applications

Thornhill

Patwardhan

Shah

2008

Journal of Process Control

193

109

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“…The TEP is composed of five major operation units: a reactor, a condenser, a compressor, a stripper and a separator. Four gaseous reactant A, C, D, E and an inert B are fed to the reactor where For example, in fault detection, we can cite [9], [30], [31]. Some fault diagnosis techniques have also been tested on the TEP [2], [32], [33], [34] with the plant-wide control structure recommended in Lyman and Georgakis [35].…”

Section: A Presentation Of the Tepmentioning

confidence: 99%

“…An interesting article is [9] in which authors make some comparisons between these different techniques. For the fault identification procedure, one of the better statistical techniques is the MYT decomposition of the T 2 statistic [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of industrial systems with bayesian networks and mutual information

Verron

Tiplica

Kobi

2007

2007 European Control Conference (ECC)

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Abstract-The purpose of this article is to present two new methods for industrial process diagnosis. These two methods are based on the use of a bayesian network. An identification of important variables is made by computing the mutual information between each variable of the system and the class variable. The performances of the two methods are evaluated on the data of a benchmark example: the Tennessee Eastman Process. Three kinds of fault are taken into account on this complex process. The challenging objective is to obtain the minimal recognition error rate for these three faults. Results are given and compared on the same data with those of other published methods.

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“…Nevertheless, the determination of the retained PCs of these traditional approaches is rather subjective or requires a priori knowledge, and none of these methods considers the performance of the fault detection in the absence of a priori knowledge. Apart from these comparative studies, the fault detection ability depends on the PCs retained in the PCA model (Kano et al, 2002). Togkalidou et al (2001) indicated that including components with smaller eigenvalues in the PCA model and excluding those with larger eigenvalues can improve the prediction quality.…”

Section: Introductionmentioning

confidence: 99%

Statistical process monitoring based on improved principal component analysis and its application to chemical processes

Tong

Yan

2013

J. Zhejiang Univ. Sci. A

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Abstract:In this paper, a novel criterion is proposed to determine the retained principal components (PCs) that capture the dominant variability of online monitored data. The variations of PCs were calculated according to their mean and covariance changes between the modeling sample and the online monitored data. The retained PCs containing dominant variations were selected and defined as correlative PCs (CPCs). The new Hotelling's T 2 statistic based on CPCs was then employed to monitor the process. Case studies on the simulated continuous stirred tank reactor and the well-known Tennessee Eastman process demonstrated the feasibility and effectiveness of the CPCs-based fault detection methods.

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Comparison of multivariate statistical process monitoring methods with applications to the Eastman challenge problem

Cited by 176 publications

References 26 publications

A continuous stirred tank heater simulation model with applications

A continuous stirred tank heater simulation model with applications

Fault diagnosis of industrial systems with bayesian networks and mutual information

Statistical process monitoring based on improved principal component analysis and its application to chemical processes

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