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...

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Vydané v:IEEE internet of things journal Ročník 12; číslo 22; s. 47658 - 47666
Hlavní autori: He, Yan-Lin, Zhou, Yang-Xiao-Yu, Xu, Yuan, Zhu, Qun-Xiong, Li, Xingyuan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 15.11.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
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Shrnutí: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 assume uniform data distributions. Additionally, limited target mode data hinders effective training. To address these issues, we propose the seasonal-trend variational transformer network (ST-VTN), a deep probabilistic model based on seasonal-trend decomposition. ST-VTN employs a Transformer-based encoder to extract seasonal and trend patterns from multimode data, enhancing latent Gaussian feature representation. A mode discriminator further improves mode-specific feature learning by distinguishing trend characteristics across modes. Built on a variational Bayesian framework, ST-VTN models the regression between latent features and quality variables. A two-stage pretraining and fine-tuning strategy enables effective use of source mode data, even with scarce target mode samples. Experiments on gas turbine and sulfur recovery datasets confirm ST-VTN's superior performance for multimode regression tasks.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2025.3602808