| 基于高阶统计和解释结构模型相结合的大型过程控制系统的故障检测(英文) |
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| 引用本文： | 耿志强,杨科,韩永明,顾祥柏. 基于高阶统计和解释结构模型相结合的大型过程控制系统的故障检测(英文)[J]. 中国化学工程学报, 2015, 23(1): 146-153. DOI: 10.1016/j.cjche.2014.10.012 |
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| 作者姓名： | 耿志强 杨科 韩永明 顾祥柏 |
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| 作者单位： | 1.College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China;2.Sinopec Engineering (Group) Co., Ltd., Beijing 100101, China |
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| 基金项目： | Supported by the National Natural Science Foundation of China(61374166);the Doctoral Fund of Ministry of Education of China(20120010110010);the Natural Science Fund of Ningbo(2012A610001) |
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| 摘 要： | Nonlinear characteristic fault detection and diagnosis method based on higher-order statistical (HOS) is an effec-tive data-driven method, but the calculation costs much for a large-scale process control system. An HOS-ISM fault diagnosis framework combining interpretative structural model (ISM) and HOS is proposed:(1) the adja-cency matrix is determined by partial correlation coefficient;(2) the modified adjacency matrix is defined by directed graph with prior knowledge of process piping and instrument diagram;(3) interpretative structural for large-scale process control system is built by this ISM method;and (4) non-Gaussianity index, nonlinearity index, and total nonlinearity index are calculated dynamical y based on interpretative structural to effectively eliminate uncertainty of the nonlinear characteristic diagnostic method with reasonable sampling period and data window. The proposed HOS-ISM fault diagnosis framework is verified by the Tennessee Eastman process and presents improvement for highly non-linear characteristic for selected fault cases.
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| 关 键 词： | High order statistics Nonlinear characteristics diagnosis Interpretative structural model TE process |
| 收稿时间： | 2013-05-07 |
Fault detection of large-scale process control system with higher-order statistical and interpretative structural model |
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| Zhiqiang Geng;Ke Yang;Yongming Han;Xiangbai Gu. Fault detection of large-scale process control system with higher-order statistical and interpretative structural model[J]. Chinese Journal of Chemical Engineering, 2015, 23(1): 146-153. DOI: 10.1016/j.cjche.2014.10.012 |
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| Authors: | Zhiqiang Geng Ke Yang Yongming Han Xiangbai Gu |
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| Affiliation: | 1.College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China;2.Sinopec Engineering (Group) Co., Ltd., Beijing 100101, China |
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| Abstract: | Nonlinear characteristic fault detection and diagnosis method based on higher-order statistical (HOS) is an effec-tive data-driven method, but the calculation costs much for a large-scale process control system. An HOS-ISM fault diagnosis framework combining interpretative structural model (ISM) and HOS is proposed:(1) the adja-cency matrix is determined by partial correlation coefficient;(2) the modified adjacency matrix is defined by directed graph with prior knowledge of process piping and instrument diagram;(3) interpretative structural for large-scale process control system is built by this ISM method;and (4) non-Gaussianity index, nonlinearity index, and total nonlinearity index are calculated dynamical y based on interpretative structural to effectively eliminate uncertainty of the nonlinear characteristic diagnostic method with reasonable sampling period and data window. The proposed HOS-ISM fault diagnosis framework is verified by the Tennessee Eastman process and presents improvement for highly non-linear characteristic for selected fault cases. |
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| Keywords: | High order statistics Nonlinear characteristics diagnosis Interpretative structural model TE process |
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