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Journal of Central South University

, Volume 23, Issue 11, pp 2896–2905 | Cite as

Multi-mode process monitoring based on a novel weighted local standardization strategy and support vector data description

  • Fu-zhou Zhao (赵付洲)
  • Bing Song (宋冰)
  • Hong-bo Shi (侍洪波)
Mechanical Engineering, Control Science and Information Engineering

Abstract

There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization (WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description (SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method’s validity, it is applied to a numerical example and a Tennessee Eastman (TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy (LNS-PCA) in multi-mode process monitoring.

Keywords

multiple operating modes weighted local standardization support vector data description multi-mode monitoring 

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Copyright information

© Central South University Press and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Fu-zhou Zhao (赵付洲)
    • 1
  • Bing Song (宋冰)
    • 1
  • Hong-bo Shi (侍洪波)
    • 1
  1. 1.Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education (East China University of Science and Technology)ShanghaiChina

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