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A novel multimode process monitoring method integrating LDRSKM with Bayesian inference

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Abstract

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.

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Authors and Affiliations

  1. National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310027, China

    Shi-jin Ren

  2. School of Computer Science & Technology, Jiangsu Normal University, Xuzhou, 221116, China

    Shi-jin Ren, Yin Liang, Xiang-jun Zhao & Mao-yun Yang

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  1. Shi-jin Ren
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  2. Yin Liang
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  4. Mao-yun Yang
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Correspondence to Shi-jin Ren.

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Project supported by the National Natural Science Foundation of China (No. 61272297)

ORCID: Shi-jin REN, http://orcid.org/0000-0002-8321-1879

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Ren, Sj., Liang, Y., Zhao, Xj. et al. A novel multimode process monitoring method integrating LDRSKM with Bayesian inference. Frontiers Inf Technol Electronic Eng 16, 617–633 (2015). https://doi.org/10.1631/FITEE.1400263

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  • DOI: https://doi.org/10.1631/FITEE.1400263

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