Process Monitoring Approach Using Fast Moving Window PCA

Wang, Xun; Krüger, Uwe; Irwin, G.W.

doi:10.1021/ie048873f

Cited by 291 publications

(179 citation statements)

References 14 publications

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“…RPCA with a forgetting factor 24 (henceforth RPCA), and MWPCA 25 have been proposed to monitor nonstationary processes. RPCA uses the idea of incorporating new observations and exponentially downweighting old ones to calculate the mean and covariance matrix used in PCA.…”

Section: Recursive Principal Component Analysismentioning

confidence: 99%

A systematic comparison of PCA‐based Statistical Process Monitoring methods for high‐dimensional, time‐dependent Processes

et al. 2016

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High‐dimensional and time‐dependent data pose significant challenges to Statistical Process Monitoring. Most of the high‐dimensional methodologies to cope with these challenges rely on some form of Principal Component Analysis (PCA) model, usually classified as nonadaptive and adaptive. Nonadaptive methods include the static PCA approach and Dynamic Principal Component Analysis (DPCA) for data with autocorrelation. Methods, such as DPCA with Decorrelated Residuals, extend DPCA to further reduce the effects of autocorrelation and cross‐correlation on the monitoring statistics. Recursive Principal Component Analysis and Moving Window Principal Component Analysis, developed for nonstationary data, are adaptive. These fundamental methods will be systematically compared on high‐dimensional, time‐dependent processes (including the Tennessee Eastman benchmark process) to provide practitioners with guidelines for appropriate monitoring strategies and a sense of how they can be expected to perform. The selection of parameter values for the different methods is also discussed. Finally, the relevant challenges of modeling time‐dependent data are discussed, and areas of possible further research are highlighted. © 2016 American Institute of Chemical Engineers AIChE J, 62: 1478–1493, 2016

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Section: Recursive Principal Component Analysismentioning

confidence: 99%

A systematic comparison of PCA‐based Statistical Process Monitoring methods for high‐dimensional, time‐dependent Processes

et al. 2016

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“…Later the catalyst depletes its catalytic property. For this reason, spent catalyst is recycled to the regenerator where it is mixed with air in a fluidized bed for regeneration of its catalytic properties [22,23]. Complete details of the mechanistic simulation model for this particular model IV FCCU can be found in McFarlane et al [24].…”

Section: Application Studies and Discussionmentioning

confidence: 99%

Nonlinear Real‐time Process Monitoring and Fault Diagnosis Based on Principal Component Analysis and Kernel Fisher Discriminant Analysis

et al. 2007

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The aim of this paper is to propose a novel real-time process monitoring and fault diagnosis method based on the principal component analysis (PCA) and kernel Fisher discriminant analysis (KFDA). There is a need to develop this method in order to overcome the inherent limitations of the current kernel FDA method. The idea of the method is to initially reduce dimensionality using PCA and then to map the score data in the reduced original space to the high-dimensional feature space via a nonlinear kernel function. Following this, the optimal Fisher feature vector and discriminant vector are extracted to perform process monitoring. If faults occur, the method uses the degree of similarity between the optimal discriminant vector presented and the optimal discriminant vector of the faults in the historical dataset to perform a diagnosis. The proposed method can effectively capture nonlinear relationships in process variables. In comparison with kernel FDA, the PCA plus kernel FDA method is more efficient and has a more rapid response when used to undertake online monitoring and fault diagnosis. In this study, the method is evaluated by applying it to the fluid catalytic cracking unit (FCCU) process. As a consequence, its effectiveness is demonstrated.

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“…Wang et al (2005) proposed a fast-moving-window algorithm for adaptive process monitoring, which incorporates the adaptation technique in a recursive PCA algorithm (Li et al, 2000) and performs through a two-step procedure to calculate the correlative matrix efficiently. In this study, the same idea is introduced to update the correlation and covariance matrices of the current score matrix to increase computational efficiency.…”

Section: Process Monitoring Proceduresmentioning

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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Process Monitoring Approach Using Fast Moving Window PCA

Cited by 291 publications

References 14 publications

A systematic comparison of PCA‐based Statistical Process Monitoring methods for high‐dimensional, time‐dependent Processes

A systematic comparison of PCA‐based Statistical Process Monitoring methods for high‐dimensional, time‐dependent Processes

Nonlinear Real‐time Process Monitoring and Fault Diagnosis Based on Principal Component Analysis and Kernel Fisher Discriminant Analysis

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

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