A Multi-rate Probabilistic Slow Feature Regression Model for Dynamic Feature Learning and Industrial Soft Sensor Development

In practical process industries, the measurements coming from different sources are collected at different sampling rates, thereby soft sensors developed using uniformly sampled measurements may result in poor prediction performance. Besides, industrial processes are inherently stochastic and most o...

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Bibliographic Details
Published in:IEEE access 2022-01, Vol.10, p.1-1
Main Authors: Zhang, Miao, Wen, Zhiwei, Zhou, Le
Format: Article
Language:English
Subjects:
Algorithms
Data models
Dynamic characteristics
expectation-maximization algorithm
Feature extraction
feature learning
Machine learning
multi-rate dynamic process
multi-rate probabilistic slow feature regression
Numerical models
Parameter modification
Performance prediction
Predictive models
Probabilistic logic
Probability theory
Process control
Regression models
Sampling
Soft sensor
Soft sensors
Statistical analysis
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Summary:In practical process industries, the measurements coming from different sources are collected at different sampling rates, thereby soft sensors developed using uniformly sampled measurements may result in poor prediction performance. Besides, industrial processes are inherently stochastic and most of them present dynamic characteristic. To cope with these issues, a multi-rate probabilistic slow feature regression (MR-PSFR) model is proposed in this paper for dynamic feature learning and soft sensor development in industrial processes. In the MR-PSFR, both input and output observation datasets with different sampling rates are used to extract the slow features, which can separate slowly and fast changing features and have a better interpretation of the outputs. Then, the expectation-maximization algorithm is modified to derive the model parameters of MR-PSFR and the quality prediction strategy for multi-rate processes is constructed. Finally, the proposed method is investigated through a numerical example and a real industrial process. The simulation results show that the extracted slow features better represent the intrinsic characteristics of the processes and the proposed model has better prediction performance for multi-rate dynamic processes than other methods.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3228048