Predictive Modeling With Multiresolution Pyramid VAE and Industrial Soft Sensor Applications

In industrial processes, the sampling rates of process variables are discrepant because of the nature of instruments and measuring demands, which forms the challenging issue, that is, the multirate modeling in the data-driven soft sensor development. In this work, a multiresolution pyramid variation...

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Veröffentlicht in:IEEE transactions on cybernetics Jg. 53; H. 8; S. 4867 - 4879
Hauptverfasser: Shen, Bingbing, Yao, Le, Ge, Zhiqiang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.08.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Data mining
Data models
Deep feature extraction
Feature extraction
feature pyramid augmentation
Industrial applications
Measuring instruments
multirate data filter
multiresolution pyramid variational autoencoder (MR-PVAE)
Numerical models
Numerical prediction
Prediction models
Predictive models
Process variables
Regression models
Sampling
soft sensor
Soft sensors
Spatial resolution
ISSN:2168-2267, 2168-2275, 2168-2275
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Zusammenfassung:In industrial processes, the sampling rates of process variables are discrepant because of the nature of instruments and measuring demands, which forms the challenging issue, that is, the multirate modeling in the data-driven soft sensor development. In this work, a multiresolution pyramid variational autoencoder (MR-PVAE) predictive model is proposed to solve this problem based on the deep feature extraction and feature pyramid augmentation. First, a multirate data filter is designed through a resolution searching strategy to turn the original process data into a multiresolution dataset. Then, the pyramid variational autoencoder (PVAE) is proposed to extract deep nonlinear features from the data with different resolutions. In PVAE, the augmented feature pyramid is constructed layer by layer to fuse extracted features from low resolution to the high. As a consequence, the extracted features with various resolutions are gathered to form the regression model, where the process information contained in data with discrepant sampling rates can be fully utilized. Due to the layer-by-layer enhanced features, the prediction accuracy of the soft sensing model are gradually improved. Meanwhile, an optimized training strategy is established to select the optimal feature pyramid for prediction. A numerical experiment and an industrial soft sensing case are given to validate the effectiveness and superiority of the proposed MR-PVAE model.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2022.3143613