Fault Detection Based on GPNMF for Industrial Process

Authors

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

Abstract

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.

Recommended Citation

Niu, Yuguang; Wang, Shilin; Lin, Zhongwei; and Li, Xiaoming (2019) "Fault Detection Based on GPNMF for Industrial Process," Journal of System Simulation: Vol. 30: Iss. 2, Article 20.
DOI: 10.16182/j.issn1004731x.joss.201802020
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss2/20

First Page

521

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201802020

Last Page

532

CLC

TP277

Recommended Citation

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.

DOI

10.16182/j.issn1004731x.joss.201802020