化工学报2016，Vol.67Issue(12)：5155-5162,8.DOI:10.11949/j.issn.0438-1157.20161199
基于LPP-GNMF算法的化工过程故障监测方法
Fault detection method for chemical process based on LPP-GNMF algorithm
摘要
Abstract
A fault detection method for chemical process based on LPP-GNMF algorithm is proposed. NMF (non-negative matrix factorization) is a novel dimensionality reduction algorithm, with characteristics of positive pure additivity of latent variables in the mechanism, thus, when compressing the data, the information can be described based on the local characteristics inner the data. Compared to the traditional multivariate statistical process monitoring methods such as principal component analysis (PCA), NMF offers a better ability for data explanation. However, firstly, NMF requires the original data to meet the requirements of non-negative, which can not be guaranteed in the actual chemical process, in order to relax the non-negative requirements of the original data, a generalized non-negative matrix factorization (GNMF) algorithm is quoted. Secondly, GNMF does not take the local structure and geometric properties into account during the process of decomposition, which may not be accurate to deal with the problem of data. Aiming at this problem, the algorithm of combining GNMF with LPP (locality preserving projection) is proposed. The proposed LPP-GNMF algorithm is applied to the Tennessee Eastman process to evaluate the monitoring performance. The simulation results show the feasibility of the proposed algorithm compared with the PCA algorithm, the NMF algorithm and the SNMF algorithm.关键词
算法/故障监测/主元分析/广义非负矩阵分解/局部投影保留/模拟Key words
algorithm/fault detection/principal component analysis/generalized non-negative matrix factorization/locality preserving projection/simulation分类
信息技术与安全科学引用本文复制引用
朱红林,王帆,侍洪波,谭帅..基于LPP-GNMF算法的化工过程故障监测方法[J].化工学报,2016,67(12):5155-5162,8.基金项目
国家自然科学基金项目（61374140）；国家自然科学基金青年科学基金项目（61403072）。@@@@supported by the National Natural Science Foundation of China (61374140) and the Young Scientists Fund of the National Natural Science Foundation of China (61403072) （61374140）
