Novel semi-supervised sparse stacked autoencoder integrated with local linear embedding for industrial soft sensing
Data-driven industrial soft sensor modeling techniques have been widely applied in predicting key variables in complex industrial processes. However, with industrial processes becoming increasingly intricate, the data they produce exhibit characteristics such as strong temporal dependencies, high di...
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United States
Elsevier Ltd
04.06.2025
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| ISSN: | 0019-0578, 1879-2022, 1879-2022 |
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| Abstract | Data-driven industrial soft sensor modeling techniques have been widely applied in predicting key variables in complex industrial processes. However, with industrial processes becoming increasingly intricate, the data they produce exhibit characteristics such as strong temporal dependencies, high dimensionality, and local structures, posing significant challenges for soft sensing. To address these issues, this paper proposes novel Semi-Supervised Sparse Stacked Autoencoder integrated with the Local Linear Embedding algorithm (SS-SAE-LLE). Unlike traditional autoencoders (AE), which capture hierarchical data features by minimizing global fitting error, SS-SAE-LLE simultaneously accounts for the spatio-temporal characteristics of the data through the local linear embedding algorithm. Moreover, it incorporates supervised tuning by leveraging labeled data and training with a semi-supervised learning framework, further improving prediction accuracy. To evaluate the feasibility of the proposed method, experiments are conducted on PTA solvent and SRU system datasets. The simulation results demonstrate that SS-SAE-LLE achieves higher prediction accuracy than other models, highlighting its applicability in the field of industrial soft sensor modeling.
•SS-SAE-LLE is proposed for data-driven industrial soft sensor modeling.•SS-SAE-LLE captures hierarchical and spatio-temporal features simultaneously.•Supervised tuning with labeled data enhances the regression performance.•SS-SAE-LLE effectively addresses high dimensionality and temporal dependencies in process data.•Experimental results on PTA solvent and SRU datasets demonstrate the superior accuracy of SS-SAE-LLE. |
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| AbstractList | Data-driven industrial soft sensor modeling techniques have been widely applied in predicting key variables in complex industrial processes. However, with industrial processes becoming increasingly intricate, the data they produce exhibit characteristics such as strong temporal dependencies, high dimensionality, and local structures, posing significant challenges for soft sensing. To address these issues, this paper proposes novel Semi-Supervised Sparse Stacked Autoencoder integrated with the Local Linear Embedding algorithm (SS-SAE-LLE). Unlike traditional autoencoders (AE), which capture hierarchical data features by minimizing global fitting error, SS-SAE-LLE simultaneously accounts for the spatio-temporal characteristics of the data through the local linear embedding algorithm. Moreover, it incorporates supervised tuning by leveraging labeled data and training with a semi-supervised learning framework, further improving prediction accuracy. To evaluate the feasibility of the proposed method, experiments are conducted on PTA solvent and SRU system datasets. The simulation results demonstrate that SS-SAE-LLE achieves higher prediction accuracy than other models, highlighting its applicability in the field of industrial soft sensor modeling.Data-driven industrial soft sensor modeling techniques have been widely applied in predicting key variables in complex industrial processes. However, with industrial processes becoming increasingly intricate, the data they produce exhibit characteristics such as strong temporal dependencies, high dimensionality, and local structures, posing significant challenges for soft sensing. To address these issues, this paper proposes novel Semi-Supervised Sparse Stacked Autoencoder integrated with the Local Linear Embedding algorithm (SS-SAE-LLE). Unlike traditional autoencoders (AE), which capture hierarchical data features by minimizing global fitting error, SS-SAE-LLE simultaneously accounts for the spatio-temporal characteristics of the data through the local linear embedding algorithm. Moreover, it incorporates supervised tuning by leveraging labeled data and training with a semi-supervised learning framework, further improving prediction accuracy. To evaluate the feasibility of the proposed method, experiments are conducted on PTA solvent and SRU system datasets. The simulation results demonstrate that SS-SAE-LLE achieves higher prediction accuracy than other models, highlighting its applicability in the field of industrial soft sensor modeling. Data-driven industrial soft sensor modeling techniques have been widely applied in predicting key variables in complex industrial processes. However, with industrial processes becoming increasingly intricate, the data they produce exhibit characteristics such as strong temporal dependencies, high dimensionality, and local structures, posing significant challenges for soft sensing. To address these issues, this paper proposes novel Semi-Supervised Sparse Stacked Autoencoder integrated with the Local Linear Embedding algorithm (SS-SAE-LLE). Unlike traditional autoencoders (AE), which capture hierarchical data features by minimizing global fitting error, SS-SAE-LLE simultaneously accounts for the spatio-temporal characteristics of the data through the local linear embedding algorithm. Moreover, it incorporates supervised tuning by leveraging labeled data and training with a semi-supervised learning framework, further improving prediction accuracy. To evaluate the feasibility of the proposed method, experiments are conducted on PTA solvent and SRU system datasets. The simulation results demonstrate that SS-SAE-LLE achieves higher prediction accuracy than other models, highlighting its applicability in the field of industrial soft sensor modeling. Data-driven industrial soft sensor modeling techniques have been widely applied in predicting key variables in complex industrial processes. However, with industrial processes becoming increasingly intricate, the data they produce exhibit characteristics such as strong temporal dependencies, high dimensionality, and local structures, posing significant challenges for soft sensing. To address these issues, this paper proposes novel Semi-Supervised Sparse Stacked Autoencoder integrated with the Local Linear Embedding algorithm (SS-SAE-LLE). Unlike traditional autoencoders (AE), which capture hierarchical data features by minimizing global fitting error, SS-SAE-LLE simultaneously accounts for the spatio-temporal characteristics of the data through the local linear embedding algorithm. Moreover, it incorporates supervised tuning by leveraging labeled data and training with a semi-supervised learning framework, further improving prediction accuracy. To evaluate the feasibility of the proposed method, experiments are conducted on PTA solvent and SRU system datasets. The simulation results demonstrate that SS-SAE-LLE achieves higher prediction accuracy than other models, highlighting its applicability in the field of industrial soft sensor modeling. •SS-SAE-LLE is proposed for data-driven industrial soft sensor modeling.•SS-SAE-LLE captures hierarchical and spatio-temporal features simultaneously.•Supervised tuning with labeled data enhances the regression performance.•SS-SAE-LLE effectively addresses high dimensionality and temporal dependencies in process data.•Experimental results on PTA solvent and SRU datasets demonstrate the superior accuracy of SS-SAE-LLE. |
| Author | Gao, Hui-Hui Xu, Yuan He, Yan-Lin Zhu, Qun-Xiong Jiang, Yu |
| Author_xml | – sequence: 1 givenname: Yan-Lin orcidid: 0000-0001-9999-3679 surname: He fullname: He, Yan-Lin email: heyl@mail.buct.edu.cn organization: College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, 100029, China – sequence: 2 givenname: Yu orcidid: 0009-0003-0550-2421 surname: Jiang fullname: Jiang, Yu email: 2022200816@mail.buct.edu.cn organization: College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, 100029, China – sequence: 3 givenname: Hui-Hui surname: Gao fullname: Gao, Hui-Hui email: gaohh@bjut.edu.cn organization: School of Information Science and Technology, Engineering Research Center of Digital Community, Ministry of Education, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China – sequence: 4 givenname: Yuan surname: Xu fullname: Xu, Yuan email: xuyuan@mail.buct.edu.cn organization: College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, 100029, China – sequence: 5 givenname: Qun-Xiong surname: Zhu fullname: Zhu, Qun-Xiong email: zhuqx@mail.buct.edu.cn organization: College of Information Science & Technology, Beijing University of Chemical Technology, Beijing, 100029, China |
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| Keywords | Industrial processes Industrial soft sensors Local features Data-driven modelling |
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