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CLC number: TP277

On-line Access: 2015-08-04

Received: 2014-07-20

Revision Accepted: 2015-05-03

Crosschecked: 2015-07-08

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Citations:  Bibtex RefMan EndNote GB/T7714


Shi-jin Ren


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Frontiers of Information Technology & Electronic Engineering  2015 Vol.16 No.8 P.617-633


A novel multimode process monitoring method integrating LDRSKM with Bayesian inference

Author(s):  Shi-jin Ren, Yin Liang, Xiang-jun Zhao, Mao-yun Yang

Affiliation(s):  1National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   sjren_phd@163.com

Key Words:  Multimode process monitoring, Local discriminant regularized soft k-means clustering, Kernel support vector data description, Bayesian inference, Tennessee Eastman process

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Shi-jin Ren, Yin Liang, Xiang-jun Zhao, Mao-yun Yang. A novel multimode process monitoring method integrating LDRSKM with Bayesian inference[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(8): 617-633.

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author="Shi-jin Ren, Yin Liang, Xiang-jun Zhao, Mao-yun Yang",
journal="Frontiers of Information Technology & Electronic Engineering",
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T1 - A novel multimode process monitoring method integrating LDRSKM with Bayesian inference
A1 - Shi-jin Ren
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A1 - Xiang-jun Zhao
A1 - Mao-yun Yang
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DOI - 10.1631/FITEE.1400263

A local discriminant regularized soft k-means (LDRSKM) method with bayesian inference is proposed for multimode process monitoring. LDRSKM extends the regularized soft k-means algorithm by exploiting the local and non-local geometric information of the data and generalized linear discriminant analysis to provide a better and more meaningful data partition. LDRSKM can perform clustering and subspace selection simultaneously, enhancing the separability of data residing in different clusters. With the data partition obtained, kernel support vector data description (KSVDD) is used to establish the monitoring statistics and control limits. Two bayesian inference based global fault detection indicators are then developed using the local monitoring results associated with principal and residual subspaces. Based on clustering analysis, bayesian inference and manifold learning methods, the within and cross-mode correlations, and local geometric information can be exploited to enhance monitoring performances for nonlinear and non-Gaussian processes. The effectiveness and efficiency of the proposed method are evaluated using the Tennessee Eastman benchmark process.

The manuscript considers a very complete combination of machine learning algorithms for multimode process monitoring, including the modified clustering (LDRSKM), dimension reduction (generalized LDA method), construction of statistics (SVDD and Bayesian method). The aspects mentioned here are such a large collection that any individual analysis for the ultimate monitoring performance is too difficult to make. This is a credible and strong presentation and deserves publication.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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