Orthogonal nonnegative matrix factorization based local hidden Markov model for multimode process monitoring

2016 ◽  
Vol 24 (7) ◽  
pp. 856-860 ◽  
Author(s):  
Fan Wang ◽  
Honglin Zhu ◽  
Shuai Tan ◽  
Hongbo Shi
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Process Monitoring ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Hidden Markov ◽  
Nonnegative Matrix

scholarly journals A Combined Markov Chain Model and Generalized Projection Nonnegative Matrix Factorization Approach for Fault Diagnosis

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Niu Yuguang ◽  
Wang Shilin ◽  
Du Ming
Keyword(s):  
Markov Chain ◽  
Process Monitoring ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Markov Chain Model ◽  
Nonnegative Matrix ◽  
Industrial Process ◽  
Chain Model ◽  
Statistical Process ◽  
Statistical Process Monitoring

The presence of sets of incomplete measurements is a significant issue in the real-world application of multivariate statistical process monitoring models for industrial process fault detection. Since the missing data in the incomplete measurements are usually correlated with some of the available variables, these measurements can be used if an efficient algorithm is presented. To resolve the problem, a novel method combining Markov chain model and generalized projection nonnegative matrix factorization (MCM-GPNMF) is proposed to detect and diagnose the faults in industrial process. The basic idea of the approach is to use MCM-GPNMF to extract the dominant variables from incomplete process data and to combine them with statistical process monitoring techniques. TG2 and SPEG statistics are defined as online monitoring quantities for fault detection and corresponding contribution plots are also considered for fault isolation. The proposed method is applied to a 1000 MW unit boiler process. The simulation results clearly illustrate the feasibility of the proposed method.

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