Soft Sensor Method based on Quality-related Virtual Sample Generation and Sample-weighted Learning

In process industry, data-driven soft sensor often faces the problem of data shortage in modeling due to factors such as high cost of label samples acquisition and high data repetition rate. The virtual sample generation (VSG) method has been proposed for data augmentation to solve the above problem...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Data Driven Control and Learning Systems Conference (Online) S. 1318 - 1324
Hauptverfasser: Dong, Shuang, Jin, Huaiping, Wang, Bin, Yang, Biao, Liu, Haipeng
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 17.05.2024
Schlagworte:
Accuracy
Data augmentation
Generative adversarial networks
Industries
Learning systems
Linear programming
Predictive models
Sample-weighted learning
Soft sensor
Soft sensors
Supervised variational autoencoder
Virtual sample generation
ISSN:2767-9861
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In process industry, data-driven soft sensor often faces the problem of data shortage in modeling due to factors such as high cost of label samples acquisition and high data repetition rate. The virtual sample generation (VSG) method has been proposed for data augmentation to solve the above problems. Most of the conventional generative models cannot generate virtual samples with labeled data, at the same time, previous data augmentation methods have ignored the quality differences of the generated virtual samples themselves. Thus, this paper proposes a soft sensor method based on quality-related virtual sample generation and sample-weighted learning (QRVSG-SWL). Firstly, this method combines the respective advantages of variational autoencoder (VAE) and generative adversarial network (GAN) to generate labeled virtual samples. Secondly, a prediction model is constructed using virtual samples to calculate the prediction accuracy and distribution differences on real labeled data. Then, sample similarity calculation under supervised latent structures.is performed. Finally, model learns virtual sample weights. The effectiveness of the proposed method is validated by the industrial chlortetracycline (CTC) fermentation process.
ISSN:2767-9861
DOI:10.1109/DDCLS61622.2024.10606560