Fault detection and diagnosis based on modified independent component analysis

Lee, Jong-Min; Qin, S. Joe; Lee, In‐Beum

doi:10.1002/aic.10978

Cited by 371 publications

(287 citation statements)

References 34 publications

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“…A total of 18 faults have been tested in the TE process. Faults 3, 9, and 15 have been suggested to be difficult to detect (Russell et al, 2000;Lee et al, 2006;Wang and He, 2010), which is also confirmed in this study. Therefore, they are not considered in this study.…”

Section: Tennessee Eastman (Te) Processsupporting

confidence: 78%

“…Other complementary multivariate statistical process monitoring methods, including canonical variate analysis, kernel PCA, dynamic PCA, and independent component analysis, have been proposed to address the limitations of PCA-or PLSbased monitoring strategies (Russell et al, 2000;Juricek et al, 2004;Lee et al, 2004a;2006). PCAbased and related monitoring methods, which build statistical models from normal operation data and partition the measurements into a principal component subspace (PCS) and a residual subspace (RS), are among the most widely used multivariate statistical methods.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Tennessee Eastman (Te) Processsupporting

confidence: 78%

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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“…Many extensions [10]- [12] of PCA have been developed to improve process monitoring performance by taking different process characteristics into consideration. However, PCA only considers the second-order statistic and cannot make use of the higher-order statistical information in non-Gaussian process data [13], [14]. Since process data are usually non-Gaussian distributed as a result of nonlinearity, operating condition shifts or other reasons [15], this limitation of PCA may result in inadequate feature extraction in nonGaussian processes.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Nonlinear and Non-Gaussian Processes Using Gaussian Mixture Model-Based Weighted Kernel Independent Component Analysis

Cai

Tian

Chen

2017

IEEE Trans. Neural Netw. Learning Syst.

100

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Abstract-Kernel independent component analysis (KICA) is widely regarded as an effective approach for nonlinear and non-Gaussian process monitoring. However, the KICA-based monitoring methods treat every kernel independent component (KIC) equally and cannot highlight the useful KICs associated with fault information. Consequently, fault information may not be explored effectively which may result in degraded fault detection performance. To overcome this problem, we propose a new nonlinear and non-Gaussian process monitoring method using Gaussian mixture model (GMM) based weighted kernel independent component analysis (WKICA). Specifically, in WKICA, GMM is firstly adopted to estimate the probabilities of the KICs extracted by KICA. The significant KICs embodying the dominant process variation are then discriminated based on the estimated probabilities and assigned with larger weights to capture the significant information during on-line fault detection. A nonlinear contribution plots method is also developed based on the idea of sensitivity analysis to help identifying the fault variables after a fault is detected. Simulation studies conducted on a simple four-variable nonlinear system and the Tennessee Eastman benchmark process demonstrate the superiority of the proposed method over the conventional KICA-based method.

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“…The d ′ independent components s j are maximized with respect to their nonGaussianity, an information based criterion for independence, often measured by kurtosis or negentropy. A few representative applications of ICA in the context of fault diagnosis are for instance (Jiang & Wang, 2004;Lee et al, 2006;Pöyhönen et al, 2003).…”

Section: Independent Component Analysismentioning

confidence: 99%

Pattern Recognition based Fault Diagnosis in Industrial Processes: Review and Application

Rauber¹,

Nascimento²,

Wandekokem³

et al. 2010

Pattern Recognition Recent Advances

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Fault detection and diagnosis based on modified independent component analysis

Cited by 371 publications

References 34 publications

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

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

Monitoring Nonlinear and Non-Gaussian Processes Using Gaussian Mixture Model-Based Weighted Kernel Independent Component Analysis

Pattern Recognition based Fault Diagnosis in Industrial Processes: Review and Application

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