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.