Isolation enhanced principal component analysis

Gertler, János; Li, Weihua; Huang, Yunbing; McAvoy, Thomas J.

doi:10.1002/aic.690450213

Cited by 185 publications

(94 citation statements)

References 17 publications

(14 reference statements)

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“…(2) and (4). It has been reported that, even though the matrix B ⁎ and Q R ' are not exactly the same, they span the same residual subspace which is orthogonal to the fault-free data [14]. Based on this similarity, the parity space fault isolation method is applied to the primary residuals in Eq.…”

Section: Standard Pcamentioning

confidence: 99%

“…The related isolation-enhanced PCA method [14] relies on algebraic transformations of the residuals represented by the last principal components, assuming that the eigenvalues have been sorted decreasingly and that the eigenvectors have been sorted accordingly. However, the basic idea behind these two methods is the same as utilizing the similarity between the PCA residual model and the explicit system model, used to generate the structured residuals by the parity space method.…”

Section: Partial Pca and Isolation-enhanced Pcamentioning

confidence: 99%

“…So-called contribution plots can give some assistance when locating the faults, but these plots do not however always provide reliable and unambiguous results especially with control loops. Improved fault isolation can be achieved with the recently introduced partial PCA method [13], and the related isolation-enhanced PCA method [14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of PCA methods with improved fault isolation capabilities on a paper machine simulator

Cheng¹,

Nikus²,

Jämsä‐Jounela³

2008

Chemometrics and Intelligent Laboratory Systems

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The work presented in this paper addresses the issues of fault detection and isolation properties of the partial PCA method and the isolation-enhanced PCA method. In order to increase the sensitivity of the residuals with respect to various faults, the structured residuals generated from both partial PCA and isolation-enhanced PCA are optimized. For the residual evaluation, the bootstrap technique is combined with the CUSUM method to achieve fast and robust detection. Three sensor faults and three actuator faults were studied using simulations employing a rigorous, first principles based, paper machine simulator. All the faults were correctly detected and isolated with both studied methods, and the results are compared with the classical T 2 and SPE contribution plot methods.

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Section: Standard Pcamentioning

confidence: 99%

Section: Partial Pca and Isolation-enhanced Pcamentioning

confidence: 99%

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Evaluation of PCA methods with improved fault isolation capabilities on a paper machine simulator

Cheng¹,

Nikus²,

Jämsä‐Jounela³

2008

Chemometrics and Intelligent Laboratory Systems

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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

192

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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“…The models may be explicit, obtained from first principles or system identification [8], [20] or implicit, obtained by principle component projection [15]. For fault isolation, some structured residuals, which respond to subsets of faults [7], [9] may be generated by algebraic transformation. In the principal component framework, the direct computation involves structured partial principal component models [3].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Statistical Process Monitoring based on Competitive Principal Component Analysis

Ramdani¹,

Mendaci²

2013

Proceedings of the 8th Conference of the European Society for Fuzzy Logic and Technology

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Traditional process monitoring techniques assume the normal operating conditions (NOC) to be distributed normally. However, for processes with more than one operating regime, building a single subspace model to monitor the whole process operation performance may not be efficient and will lead to high rate of missing alarm. To handle this situation, a monitoring strategy using multiple subspace models is presented in this paper. From the experimental results using a simulation model of a continuous flow aerated bioreactor for wastewater treatment in pulp and paper industry it has been shown that the proposed approach is very promising.

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Isolation enhanced principal component analysis

Cited by 185 publications

References 17 publications

Evaluation of PCA methods with improved fault isolation capabilities on a paper machine simulator

Evaluation of PCA methods with improved fault isolation capabilities on a paper machine simulator

A continuous stirred tank heater simulation model with applications

Nonlinear Statistical Process Monitoring based on Competitive Principal Component Analysis

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