Novel Semi-Supervised Seasonal-Trend VTN for Multimode Process IoT Soft Sensing

The rapid development of soft sensors has significantly enhanced industrial operations by promoting sustainability, safety, and efficiency. However, modern process industries involve highly dynamic systems with multimode and nonlinear data, posing challenges for conventional soft sensor models that...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE internet of things journal Jg. 12; H. 22; S. 47658 - 47666
Hauptverfasser:	He, Yan-Lin, Zhou, Yang-Xiao-Yu, Xu, Yuan, Zhu, Qun-Xiong, Li, Xingyuan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 15.11.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Autoencoders Data models Feature extraction Gas turbines Internet of Things Multimode process Predictive models Probabilistic models soft sensing Soft sensors Statistical analysis Time series analysis Training transfer learning transformer Transformers variational Bayesian network
ISSN:	2327-4662, 2327-4662
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!