Online monitoring of nonlinear multivariate industrial processes using filtering KICA–PCA

Fan, Jicong; Qin, S. Joe; Wang, Youqing

doi:10.1016/j.conengprac.2013.06.017

Cited by 98 publications

(60 citation statements)

References 37 publications

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“…Other research streams are more closely related to performance measures and include the distance of reference samples to the furthest and nearest neighbors (DFN) [40], and include intelligent optimization, e.g. genetic algorithms (GA) [41,42]. Each method, however, requires a preestimate of n.…”

Section: Other More General Methods For Estimating σmentioning

confidence: 99%

Cross-validatory framework for optimal parameter estimation of KPCA and KPLS models

Krüger

et al. 2017

Chemometrics and Intelligent Laboratory Systems

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Section: Other More General Methods For Estimating σmentioning

confidence: 99%

Cross-validatory framework for optimal parameter estimation of KPCA and KPLS models

Krüger

et al. 2017

Chemometrics and Intelligent Laboratory Systems

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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%

“…2) Based on the training data, construct the KICA model to obtain the training samples of the KICs. 3) Fit the probability density function of each KIC by using the GMM (17) and estimate the GMM parameters based on the training samples of each KIC using (19) to (22). 4) Calculate the probabilities of the training samples of each KIC using (23) and (24), and determine the probability threshold for each KIC.…”

Section: The Off-line Modeling Stagementioning

confidence: 99%

“…Taking both the process nonlinearity and multimodality into consideration, Zhang et al [4] introduced the Kronecker product to modify the monitoring matrices, and proposed a multimode KICA method for process monitoring. Fan et al [22] proposed a filtering KICA-PCA method to improve the process monitoring performance, which applies genetic algorithm to determine the kernel parameter and adopts the exponentially weighted moving average technique to filter the monitoring statistics. To solve the monitoring problem of nonlinear batch processes, Tian et al [23] proposed a multiway KICA method, which unfolds the three-way dataset into the two-way one and then selects representative feature samples to construct the KICA monitoring model.…”

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.

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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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“…33 For nonlinear methods, variable contribution can be characterized by the partial derivative and larger partial derivative means larger contribution. 33−35 To recognize the fault variable, contribution analysis of multimode processes in this paper can be obtained by partial derivatives.…”

Section: Industrial and Engineering Chemistry Researchmentioning

confidence: 99%

Monitoring of Multimode Processes Based on Subspace Decomposition

Ли¹,

Zhou²,

Shi³

et al. 2015

Ind. Eng. Chem. Res.

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This paper presents a new monitoring method for multimode processes based on subspace decomposition. In the proposed method, the influence of quality variables and multimode information are considered in multimode processes modeling, which is crucially important to ensure industrial production safety and quality stabilization. Process data are decomposed into the global common subspace and the local specific subspace and monitoring is performed in each subspace to simplify the model structure. Two experiments: penicillin fermentation processes and practical foods industrial production processes, have been used to demonstrate the excellent performance of the proposed method.

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Online monitoring of nonlinear multivariate industrial processes using filtering KICA–PCA

Cited by 98 publications

References 37 publications

Cross-validatory framework for optimal parameter estimation of KPCA and KPLS models

Cross-validatory framework for optimal parameter estimation of KPCA and KPLS models

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

Monitoring of Multimode Processes Based on Subspace Decomposition

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