Adversarial Training-Based Deep Layer-Wise Probabilistic Network for Enhancing Soft Sensor Modeling of Industrial Processes

Improving the robustness of the soft sensor model of industrial processes is an important yet challenging problem for a large amount of noise interference and missing data in practical industrial data. In this article, an adversarial training-based deep supervised variational autoencoder (Adv-DSVAE)...

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Veröffentlicht in:IEEE transactions on systems, man, and cybernetics. Systems Jg. 54; H. 2; S. 1 - 13
Hauptverfasser: Xie, Yongfang, Wang, Jie, Xie, Shiwen, Chen, Xiaofang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.02.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Ablation
Adversarial training
aluminum electrolysis process
Data models
Electrolysis
Feature extraction
Missing data
Probabilistic logic
Probability theory
Representations
Robustness
Sensors
soft sensor modeling
Soft sensors
Stacking
supervised variational autoencoder (SVAE)
Training
ISSN:2168-2216, 2168-2232
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Zusammenfassung:Improving the robustness of the soft sensor model of industrial processes is an important yet challenging problem for a large amount of noise interference and missing data in practical industrial data. In this article, an adversarial training-based deep supervised variational autoencoder (Adv-DSVAE) is proposed to enhance the performance of industrial soft sensor models. Specifically, a supervised variational autoencoder (SVAE) is first designed to extract the quality-relevant feature representation. Then, a deep SVAE (DSVAE) model is constructed by stacking the hidden features extracted by SVAE, such that a high-level output-related feature representation can be captured. In this way, the missing data situation can be handled by the probabilistic latent feature representation extracted in DSVAE. To improve the robustness of a DSVAE-based soft sensor model, an adversarial training method is designed, in which adversarial examples are generated by adding perturbations to the last hidden feature of DSVAE, such that the model can perform well on both clean and perturbed feature representations. We further provide theoretical convergence analysis for the proposed Adv-DSVAE to guarantee its successful practical application. The ablation studies confirm that industrial quality prediction using the adversarial training strategy can ensure better robustness. Case studies on both the debutanizer column process and the real-world aluminum electrolysis process validate the superiority of Adv-DSVAE.
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ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2023.3322195