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Semi-Supervised Incremental Soft Sensor Model With Spatiotemporal Graph Regularization for Process Industry
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DOI：10.1109/TIM.2024.3500040.png)
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Abstract
En 中文
In some process industries, process variables are readily measurable, whereas real-time detection of industrial indices poses challenges. This discrepancy results in the lack of a one-to-one correspondence between process variables and indices, leading to a large number of incomplete samples in process industry datasets. This article introduces an incremental random recurrent neural network (IRRNN) that combines incremental architecture construction with random weights. Based on the IRRNN, a semi-supervised IRRNN (SS-IRRNN) approach for soft sensor modeling is proposed. SS-IRRNN leverages both labeled and unlabeled samples as inputs and utilizes resampling techniques to obtain labeled hidden states from the hidden layer of the IRRNN. These labeled states are then used to optimize the output weights of the IRRNN. Furthermore, SS-IRRNN with spatiotemporal graph (SGSS-IRRNN) regularization is designed to enhance the capture of spatiotemporal information in incomplete datasets. Numerical simulation experiments and soft sensing experiments on hematite grinding processes demonstrate that the proposed methods exhibit sound performance when dealing with incomplete datasets.
Keywords:
Soft sensors
Industries
Spatiotemporal phenomena
Accuracy
Numerical models
Nonlinear dynamical systems
Data models
Adaptation models
Semisupervised learning
Real-time systems
Incremental learning
random weight neural network
recurrent neural networks (RNNs)
semi-supervised learning (SSL)
soft sensor
Journal
IF:
5.9
Papers:
1.9W
Citations:
5.8W
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