Performance-driven ensemble learning ICA model for improved non-Gaussian process monitoring

Ge, Zhiqiang; Song, Zhihuan

doi:10.1016/j.chemolab.2013.02.001

Cited by 81 publications

(49 citation statements)

References 32 publications

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“…The Tennessee Eastman (TE) industrial process [35] is a well-known benchmark process for testing process monitoring methods [1], [5], [7]- [10], [14], [17], [19], [20], [22]. The flowchart of the TE process is depicted in Fig.…”

Section: B the Tennessee Eastman Industrial Processmentioning

confidence: 99%

“…An empirical method for choosing the value of η is provided here. As the false alarm rate, defined as the percentage of the false alarming samples in all the normal operation samples, is vital for measuring the reliability of fault detection [4], [5], [8], [14], it is taken as the evaluation index for choosing an appropriate value of η. Specifically, it is suggested to set the value of η to 0.3 initially and to check whether the false alarm rate of the normal-operation validating data is in the acceptable confidence range.…”

Section: A Fault Detection Based On Wkicamentioning

confidence: 99%

“…As no prior knowledge is available regarding the distributions of the KICs extracted by KICA in the built monitoring statistics, both the α confidence limit W I 2 lim,α for the monitoring statistic W I 2 t and the α confidence limit W Q lim,α for the monitoring statistic W Q t are determined by the well-known kernel density estimation (KDE) method [4], [5], [13], [14].…”

Section: A Fault Detection Based On Wkicamentioning

confidence: 99%

“…More recently, independent component analysis (ICA), as a newly emerging multivariate statistical approach, has exhibited tremendous potential for non-Gaussian process monitoring [4], [5], [16], [17]. In comparison to PCA, ICA can effectively utilize the higher-order information in non-Gaussian process data and extracts mutually independent latent variables known as independent components (ICs) from the original process variables.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: B the Tennessee Eastman Industrial Processmentioning

confidence: 99%

Section: A Fault Detection Based On Wkicamentioning

confidence: 99%

Section: A Fault Detection Based On Wkicamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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show abstract

“…Ge and song [6] analyzed fault samples and presented a novel monitoring performance-driven IC selection method. Moreover, if all the selected PPCs are used to construct the 2 statistic with the same importance, a large amount of useless information might cover up the fault-relevant information; which makes fault detection results undesirable.…”

Section: Introductionmentioning

confidence: 99%

Joint Probability Density and Weighted Probabilistic PCA Based on Coefficient of Variation for Multimode Process Monitoring

Zhu¹,

Huang²,

Yan³

2017

dtcse

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Abstract. For probabilistic monitoring of multimode processes, this paper introduced a monitoring scheme that integrates joint probability density and weighted probabilistic principal component analysis based on coefficient of variation (CV-WPPCA). A joint probability based on 2 statistic was constructed for mode identification. After it concentrated maximum fault-relevant information into dominant subspace by identifying and extracting important noise factors from the residual subspace, the new approach utilized a weighting strategy based on coefficient of variation method to highlight the useful information in the reconstructed dominant subspace. A case study on the Tennessee Eastman process was applied to demonstrate the efficiency of the proposed method.

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Just‐in‐time reorganized PCA integrated with SVDD for chemical process monitoring

Jiang

Yan

2014

AIChE Journal

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Although principal component analysis (PCA) is widely used for chemical process monitoring, improvements in the selection of principal components (PCs) are still needed. Given that the determination of complicated and changing fault information is not guaranteed using offline-selected PCs, this study proposes a just-in-time reorganized PCA model that objectively selects the PCs online for process monitoring. The importance of the PCs is evaluated online by kernel density estimation. The PCs indicating more varied information are then selected to reorganize the PCA model. Given that the most useful fault information is concentrated, support vector data description is used to replace traditional statistics, thereby relaxing the Gaussian assumption of the process data. The monitoring performances of the proposed method are evaluated under three cases. Compared with conventional PCA methods, more varied information is captured online, and the monitoring performances are improved. V C 2014 American Institute of Chemical Engineers AIChE J, 60: 949-965, 2014

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Performance-driven ensemble learning ICA model for improved non-Gaussian process monitoring

Cited by 81 publications

References 32 publications

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

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

Joint Probability Density and Weighted Probabilistic PCA Based on Coefficient of Variation for Multimode Process Monitoring

Just‐in‐time reorganized PCA integrated with SVDD for chemical process monitoring

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