Statistical process monitoring based on modified nonnegative matrix factorization

2015 ◽  
Vol 28 (3) ◽  
pp. 1359-1370 ◽  
Author(s):  
Nan Li ◽  
Yupu Yang
Keyword(s):  
Process Monitoring ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Statistical Process ◽  
Statistical Process Monitoring

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.

scholarly journals Multimode Process Monitoring Method Based on Multiblock Projection Nonnegative Matrix Factorization

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Yan Wang ◽  
Yu-Bo Zhao ◽  
Chuang Li ◽  
Chuan-Qian Zhu ◽  
Shuai-shuai Han ◽  
...  
Keyword(s):  
Process Monitoring ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Information Criterion ◽  
Training Data ◽  
Monitoring Methods ◽  
Process Data ◽  
Monitoring Method ◽  
Data Set

A multimode process monitoring method based on multiblock projection nonnegative matrix factorization (MPNMF) is proposed for traditional process monitoring methods which often adopt global model of data and ignore local information of data. Firstly, the training data set of each mode is partitioned by the complete link algorithm and the multivariate data space is divided into several subblocks. Then, the projection nonnegative matrix factorization (PNMF) algorithm is used to model each subspace of each mode separately. A joint probabilistic statistic index is defined to identify the running modes of the process data. Finally, the Bayesian information criterion (BIC) is used to synthesize the statistics of each subblock and construct a new statistic for process monitoring. The proposed process monitoring method is applied to the TE process to verify its effectiveness.

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