基于GPNMF的工业过程故障检测

doi:10.16182/j.issn1004731x.joss.201802020

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (2): 521-532.doi: 10.16182/j.issn1004731x.joss.201802020

基于GPNMF的工业过程故障检测

牛玉广¹, 王世林², 林忠伟^1,2, 李晓明³

1.华北电力大学新能源电力系统国家重点实验室,北京 102206;
2.华北电力大学控制与计算机工程学院,北京 102206;
3.东北电力大学自动化工程学院,吉林 132012

收稿日期:2017-01-10 出版日期:2018-02-08 发布日期:2019-01-02
作者简介:牛玉广(1964-),男,河南,博士,教授,研究方向为新能源电力系统建模与控制,大型火电机组优化控制与故障诊断;王世林(1987-),男,河北,博士生,研究方向为大型火电机组控制系统故障诊断。
基金资助:
国家自然科学基金青年基金(51606033)

Fault Detection Based on GPNMF for Industrial Process

Niu Yuguang¹, Wang Shilin², Lin Zhongwei^1,2, Li Xiaoming³

1.State Key Laboratory for Alternate Electric Power System with Renewable Energy Source, North China Electric Power University, Beijing 102206, China;
2.School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China;
3.School of Automation Engineering, Northeast Dianli University, Jilin 132012, China

Received:2017-01-10 Online:2018-02-08 Published:2019-01-02

摘要/Abstract

摘要： 非负矩阵分解(NMF)作为一种新的矩阵降维技术已经广泛应用于不同的科学领域。NMF要求待分解矩阵元素均为非负值,但是,实际工业过程所产生的运行数据并不能保证都是非负的。针对这一问题,提出一种新算法——广义投影非负矩阵分解(GPNMF)。利用GPNMF提取测量矩阵中包含过程运行特征的隐变量信息,使之与过程监控技术相结合来实现工业过程的故障检测,并构建相应的贡献图法来实现故障分离。将所提算法应用于国内某电厂1 000 MW机组锅炉系统,实验结果验证了新方法对故障检测及分离的有效性。

关键词: 故障检测, 故障分离, 广义投影非负矩阵分解, 锅炉过程

Abstract: As a newly dimension reduction technique, non-negative matrix factorization (NMF) has been applied in varying research areas. NMF methods require the original data non-negative. However, the operating data of industrial process maybe not satisfy this restriction. To resolve the problem, a new method is presented, which can be called as generalized projection non-negative matrix factorization (GPNMF). We use GPNMF to extract the latent variables that drive a process and to combine them with process monitoring techniques for fault detection. The corresponding contribution plots are defined for fault isolation. The proposed method is applied to a 1 000 MW unit boiler process. The simulation results clearly illustrate the feasibility of the proposed method.

Key words: fault detection, fault isolation, generalized projection non-negative matrix factorization, boiler process

中图分类号:

TP277

牛玉广, 王世林, 林忠伟, 李晓明. 基于GPNMF的工业过程故障检测[J]. 系统仿真学报, 2018, 30(2): 521-532.

Niu Yuguang, Wang Shilin, Lin Zhongwei, Li Xiaoming. Fault Detection Based on GPNMF for Industrial Process[J]. Journal of System Simulation, 2018, 30(2): 521-532.

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基于GPNMF的工业过程故障检测

Fault Detection Based on GPNMF for Industrial Process

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