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Temporal-Spatial Neighborhood Enhanced Sparse Autoencoder for Nonlinear Dynamic Process Monitoring
- Source :
- Processes, Vol 8, Iss 1079, p 1079 (2020), Processes, Volume 8, Issue 9
- Publication Year :
- 2020
- Publisher :
- MDPI AG, 2020.
-
Abstract
- 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.
- Subjects :
- Computer science
Bayesian probability
Feature extraction
Bioengineering
Sample (statistics)
02 engineering and technology
temporal-spatial neighborhood
lcsh:Chemical technology
Bayesian
Fault detection and isolation
lcsh:Chemistry
020401 chemical engineering
0202 electrical engineering, electronic engineering, information engineering
Chemical Engineering (miscellaneous)
lcsh:TP1-1185
dynamic process
0204 chemical engineering
business.industry
Process Chemistry and Technology
020208 electrical & electronic engineering
Autocorrelation
Process (computing)
Nonlinear dimensionality reduction
Pattern recognition
Autoencoder
fault detection
sparse autoencoder
lcsh:QD1-999
Artificial intelligence
business
Subjects
Details
- Language :
- English
- ISSN :
- 22279717
- Volume :
- 8
- Issue :
- 1079
- Database :
- OpenAIRE
- Journal :
- Processes
- Accession number :
- edsair.doi.dedup.....6068973cab269b86763b284e72873cd4