文献类型：期刊文献

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

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

作者：Zhao Fu-zhou[1];Song Bing[1];Shi Hong-bo[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2016

卷号：23

期号：11

起止页码：2896

中文期刊名：Journal of Central South University

外文期刊名：JOURNAL OF CENTRAL SOUTH UNIVERSITY

收录：CSTPCD;;EI(收录号：20165003125023);Scopus;WOS:【SCI-EXPANDED(收录号:WOS:000390025300018)】；CSCD:【CSCD2015_2016】；

基金：Foundation item: Project(61374140) supported by the National Natural Science Foundation of China

语种：英文

中文关键词：multiple operating modes;weighted local standardization;support vector data description;multi-mode monitoring

外文关键词：multiple operating modes; weighted local standardization; support vector data description; multi-mode monitoring

摘要：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and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution.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.
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.