Nonlinear Dynamic Process Monitoring Based on Discriminative Denoising Autoencoder and Canonical Variate Analysis

Modern industrial processes are characterized by increasing complexity, often exhibiting pronounced dynamic behaviors and significant nonlinearity. Addressing these dynamic and nonlinear characteristics is essential for effective process monitoring. However, many existing methods for process monitor...

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Bibliographic Details
Published in:Actuators 2024-11, Vol.13 (11), p.440
Main Authors: Liang, Jun, Liu, Daoguang, Zhan, Yinxiao, Fan, Jiayu
Format: Article
Language:English
Subjects:
canonical variate analysis (CVA)
Classification
Correlation analysis
Data processing
denoising autoencoder (DAE)
Discriminant analysis
dynamics
Effectiveness
Fault diagnosis
Feature extraction
Methods
Monitoring
Noise reduction
Nonlinear dynamics
nonlinear process
Nonlinearity
process monitoring
Redundancy
Variables
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Summary:Modern industrial processes are characterized by increasing complexity, often exhibiting pronounced dynamic behaviors and significant nonlinearity. Addressing these dynamic and nonlinear characteristics is essential for effective process monitoring. However, many existing methods for process monitoring and fault diagnosis are insufficient in handling these challenges. In this article, we present a novel process monitoring approach, CVA-DisDAE, which integrates an improved Denoising Autoencoder (DAE) with Canonical Variate Analysis (CVA) to address the challenges posed by dynamic behaviors and nonlinear relationships in industrial processes. First, CVA is employed to reduce data dimensionality and minimize information redundancy by maximizing correlations between past and future observations, thereby effectively capturing process dynamics. Following this, we introduce a discriminative DAE model (DisDAE) designed to serve as a semi-supervised denoising autoencoder for precise feature extraction. This is achieved by incorporating both between-class separability and within-class variability into the traditional DAE framework. The key distinction between the proposed DisDAE and the conventional DAE lies in the integration of a linear discriminant analysis (LDA) penalty into the DAE’s loss function, resulting in extracted features that are more conducive to fault classification. Finally, we validate the effectiveness of the proposed semi-supervised dynamic process monitoring approach through its application to the Tennessee Eastman benchmark process, demonstrating its superior performance.
ISSN:2076-0825
2076-0825
DOI:10.3390/act13110440