Dynamic Process Monitoring

2012 ◽  
pp. 131-146
Author(s):  
Zhiqiang Ge ◽  
Zhihuan Song
Keyword(s):  
Process Monitoring ◽  
Dynamic Process

Robust dynamic process monitoring based on sparse representation preserving embedding

2016 ◽  
Vol 40 ◽  
pp. 119-133 ◽  
Author(s):  
Zhibo Xiao ◽  
Huangang Wang ◽  
Junwu Zhou
Keyword(s):  
Sparse Representation ◽  
Process Monitoring ◽  
Dynamic Process

scholarly journals Temporal-Spatial Neighborhood Enhanced Sparse Autoencoder for Nonlinear Dynamic Process Monitoring

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1079
Author(s):  
Nanxi Li ◽  
Hongbo Shi ◽  
Bing Song ◽  
Yang Tao
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Process Monitoring ◽  
Dynamic Process ◽  
Process Data ◽  
Detection Scheme ◽  
Current Time ◽  
Neighborhood Information ◽  
Sparse Autoencoder ◽  
Current Sample

Data-based process monitoring methods have received tremendous attention in recent years, and modern industrial process data often exhibit dynamic and nonlinear characteristics. Traditional autoencoders, such as stacked denoising autoencoders (SDAEs), have excellent nonlinear feature extraction capabilities, but they ignore the dynamic correlation between sample data. Feature extraction based on manifold learning using spatial or temporal neighbors has been widely used in dynamic process monitoring in recent years, but most of them use linear features and do not take into account the complex nonlinearities of industrial processes. Therefore, a fault detection scheme based on temporal-spatial neighborhood enhanced sparse autoencoder is proposed in this paper. Firstly, it selects the temporal neighborhood and spatial neighborhood of the sample at the current time within the time window with a certain length, the spatial similarity and time serial correlation are used for weighted reconstruction, and the reconstruction combines the current sample as the input of the sparse stack autoencoder (SSAE) to extract the correlation features between the current sample and the neighborhood information. Two statistics are constructed for fault detection. Considering that both types of neighborhood information contain spatial-temporal structural features, Bayesian fusion strategy is used to integrate the two parts of the detection results. Finally, the superiority of the method in this paper is illustrated by a numerical example and the Tennessee Eastman process.

An approach for multimode dynamic process monitoring using Bayesian inference

2012 ◽  
Vol 45 (20) ◽  
pp. 1011-1016 ◽  
Author(s):  
Adel Haghani ◽  
Steven X. Ding ◽  
Haiyang Hao ◽  
Shen Yin ◽  
Torsten Jeinsch
Keyword(s):  
Bayesian Inference ◽  
Process Monitoring ◽  
Dynamic Process
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