Dynamic Feature Extraction and Semi-Supervised Soft Sensor Model Based on SCINet for Industrial and Transportation Processes
In industrial processes, dynamic changes are one of the factors restricting the performance of soft sensor models. Meanwhile, the inconsistency of sensor sampling rates often leads to the problem of mismatch between process variables and quality variables. This paper proposes a semi-supervised soft...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Applied System Innovation |
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| Online Access: | https://www.mdpi.com/2571-5577/8/3/73 |
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| author | Jun Wang Changjian Qi Xing Luo Shihao Deng Qi Lei |
| author_facet | Jun Wang Changjian Qi Xing Luo Shihao Deng Qi Lei |
| author_sort | Jun Wang |
| collection | DOAJ |
| description | In industrial processes, dynamic changes are one of the factors restricting the performance of soft sensor models. Meanwhile, the inconsistency of sensor sampling rates often leads to the problem of mismatch between process variables and quality variables. This paper proposes a semi-supervised soft sensor modeling method based on sample convolution and interactive networks (SCINet). To extract the dynamic information of industrial processes more fully, an unsupervised time series dynamic feature extractor was designed based on SCINet and an autoencoder, and the feature extractor was trained using complete data. The dynamic features encoded by the dynamic feature extractor were transferred to the eXtreme Gradient Boosting (XGBoost) ensemble model with strong generalization ability. The semi-supervised soft measurement model SSCI-XGBoost was established. The effectiveness of dynamic feature transfer and model performance improvement was verified on the industrial process dataset. |
| format | Article |
| id | doaj-art-dca81dfb1b824704a31a32f5613ea026 |
| institution | Kabale University |
| issn | 2571-5577 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied System Innovation |
| spelling | doaj-art-dca81dfb1b824704a31a32f5613ea0262025-08-20T03:32:31ZengMDPI AGApplied System Innovation2571-55772025-05-01837310.3390/asi8030073Dynamic Feature Extraction and Semi-Supervised Soft Sensor Model Based on SCINet for Industrial and Transportation ProcessesJun Wang0Changjian Qi1Xing Luo2Shihao Deng3Qi Lei4CRRC Nanjing Puzhen Vehicle Co., Ltd., Nanjing 210031, ChinaCRRC Nanjing Puzhen Vehicle Co., Ltd., Nanjing 210031, ChinaMain Campus, School of Automation, Electrical EngineeringCentral South University, Changsha 410083, ChinaMain Campus, School of Automation, Electrical EngineeringCentral South University, Changsha 410083, ChinaMain Campus, School of Automation, Electrical EngineeringCentral South University, Changsha 410083, ChinaIn industrial processes, dynamic changes are one of the factors restricting the performance of soft sensor models. Meanwhile, the inconsistency of sensor sampling rates often leads to the problem of mismatch between process variables and quality variables. This paper proposes a semi-supervised soft sensor modeling method based on sample convolution and interactive networks (SCINet). To extract the dynamic information of industrial processes more fully, an unsupervised time series dynamic feature extractor was designed based on SCINet and an autoencoder, and the feature extractor was trained using complete data. The dynamic features encoded by the dynamic feature extractor were transferred to the eXtreme Gradient Boosting (XGBoost) ensemble model with strong generalization ability. The semi-supervised soft measurement model SSCI-XGBoost was established. The effectiveness of dynamic feature transfer and model performance improvement was verified on the industrial process dataset.https://www.mdpi.com/2571-5577/8/3/73soft sensorsample convolution and interactive networks (SCINet)dynamic feature extractor |
| spellingShingle | Jun Wang Changjian Qi Xing Luo Shihao Deng Qi Lei Dynamic Feature Extraction and Semi-Supervised Soft Sensor Model Based on SCINet for Industrial and Transportation Processes Applied System Innovation soft sensor sample convolution and interactive networks (SCINet) dynamic feature extractor |
| title | Dynamic Feature Extraction and Semi-Supervised Soft Sensor Model Based on SCINet for Industrial and Transportation Processes |
| title_full | Dynamic Feature Extraction and Semi-Supervised Soft Sensor Model Based on SCINet for Industrial and Transportation Processes |
| title_fullStr | Dynamic Feature Extraction and Semi-Supervised Soft Sensor Model Based on SCINet for Industrial and Transportation Processes |
| title_full_unstemmed | Dynamic Feature Extraction and Semi-Supervised Soft Sensor Model Based on SCINet for Industrial and Transportation Processes |
| title_short | Dynamic Feature Extraction and Semi-Supervised Soft Sensor Model Based on SCINet for Industrial and Transportation Processes |
| title_sort | dynamic feature extraction and semi supervised soft sensor model based on scinet for industrial and transportation processes |
| topic | soft sensor sample convolution and interactive networks (SCINet) dynamic feature extractor |
| url | https://www.mdpi.com/2571-5577/8/3/73 |
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