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

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 capa...

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Bibliographic Details
Published in:Processes Vol. 8; no. 9; p. 1079
Main Authors: Li, Nanxi, Shi, Hongbo, Song, Bing, Tao, Yang
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.09.2020
Subjects:
Algorithms
Bayesian analysis
Correlation
Fault detection
Feature extraction
Machine learning
Manifolds (mathematics)
Methods
Monitoring
Neighborhoods
Neural networks
Noise reduction
Nonlinear dynamics
Principal components analysis
Reconstruction
Sparsity
Spatial discrimination learning
Windows (intervals)
United States > US
Tennessee
ISSN:2227-9717, 2227-9717
Online Access:Get full text
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Summary: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.
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ISSN:2227-9717
2227-9717
DOI:10.3390/pr8091079