Deep quality-related feature extraction for soft sensing modeling: A deep learning approach with hybrid VW-SAE
Soft sensors have been extensively used to predict difficult-to-measure quality variables for effective modeling, control and optimization of industrial processes. To construct accurate soft sensors, it is significant to carry out feature extraction for massive high-dimensional process data. Recentl...
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| Vydáno v: | Neurocomputing (Amsterdam) Ročník 396; s. 375 - 382 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
05.07.2020
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | Soft sensors have been extensively used to predict difficult-to-measure quality variables for effective modeling, control and optimization of industrial processes. To construct accurate soft sensors, it is significant to carry out feature extraction for massive high-dimensional process data. Recently, deep learning has been introduced for feature representation in process data modeling. However, most of them cannot capture deep quality-related features for output prediction. In this paper, a hybrid variable-wise weighted stacked autoencoder (HVW-SAE) is developed to learn quality-related features for soft sensor modeling. By measuring the linear Pearson and nonlinear Spearman correlations for variables at the input layer with the quality variable at each encoder, a corresponding weighted reconstruction objective function is designed to successively pretrain the deep networks. With the constraint of preferential reconstruction for more quality-related variables, it can ensure that the learned features contain more information for quality prediction. Finally, the effectiveness of the proposed HVW-SAE based soft sensor method is validated on an industrial debutanizer column process. |
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| AbstractList | Soft sensors have been extensively used to predict difficult-to-measure quality variables for effective modeling, control and optimization of industrial processes. To construct accurate soft sensors, it is significant to carry out feature extraction for massive high-dimensional process data. Recently, deep learning has been introduced for feature representation in process data modeling. However, most of them cannot capture deep quality-related features for output prediction. In this paper, a hybrid variable-wise weighted stacked autoencoder (HVW-SAE) is developed to learn quality-related features for soft sensor modeling. By measuring the linear Pearson and nonlinear Spearman correlations for variables at the input layer with the quality variable at each encoder, a corresponding weighted reconstruction objective function is designed to successively pretrain the deep networks. With the constraint of preferential reconstruction for more quality-related variables, it can ensure that the learned features contain more information for quality prediction. Finally, the effectiveness of the proposed HVW-SAE based soft sensor method is validated on an industrial debutanizer column process. |
| Author | Gui, Weihua Yuan, Xiaofeng Wang, Yalin Yang, Chunhua Ou, Chen |
| Author_xml | – sequence: 1 givenname: Xiaofeng orcidid: 0000-0002-9072-7179 surname: Yuan fullname: Yuan, Xiaofeng email: yuanxf@csu.edu.cn – sequence: 2 givenname: Chen surname: Ou fullname: Ou, Chen – sequence: 3 givenname: Yalin surname: Wang fullname: Wang, Yalin – sequence: 4 givenname: Chunhua surname: Yang fullname: Yang, Chunhua – sequence: 5 givenname: Weihua surname: Gui fullname: Gui, Weihua |
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| Cites_doi | 10.1109/TNNLS.2016.2599820 10.1109/TIP.2017.2781299 10.1109/TIE.2018.2803727 10.1109/TCST.2016.2579609 10.1109/TII.2015.2509247 10.1109/TIM.2018.2810678 10.1109/TIM.2017.2658158 10.1002/aic.14663 10.1109/TIE.2017.2739691 10.1109/ACCESS.2017.2756872 10.1109/TCYB.2016.2536638 10.1109/TII.2016.2610839 10.1016/j.jprocont.2017.06.002 10.1109/TCST.2016.2550426 10.1109/TIE.2017.2698422 10.1109/TII.2018.2809730 10.1016/j.jprocont.2013.05.007 10.1016/j.conengprac.2004.04.013 10.1016/j.compchemeng.2008.12.012 10.1109/TII.2016.2612640 10.1016/j.compchemeng.2008.05.019 10.1109/TIE.2017.2733501 10.1002/cem.3040 10.1109/TIE.2015.2466557 10.1021/ie4041252 10.1109/TIE.2017.2733443 10.1016/j.jprocont.2013.03.008 10.1109/TIM.2006.887331 |
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| Keywords | Deep learning Stacked autoencoder (SAE) Soft sensor Hybrid variable-wise weighted SAE (HVW-SAE) Feature representation |
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| References | Yu, Hong, Rui, Tao (bib0026) 2018; 65 Samek, Binder, Montavon, Lapuschkin, Muller (bib0028) 2016; 28 Chen, Ding, Peng, Yang, Gui (bib0012) 2018; 65 Fortuna, Graziani, Rizzo, Xibilia (bib0035) 2007 Fortuna, Graziani, Xibilia (bib0034) 2005; 13 Ge, Chen (bib0002) 2017; 12 Jia, Shao, Li, Zhao, Fu (bib0031) 2018 Yuan, Huang, Wang, Yang, Gui (bib0032) 2018; 14 Hauke, Kossowski (bib0033) 2011; 30 Bengio, Lamblin, Popovici, Larocchelle (bib0025) 2006 Liu, Chen (bib0018) 2013; 23 Majumdar, Tripathi (bib0029) 2017 Lee, Chang (bib0027) 2016; 13 Huang, Qi, Murshed (bib0014) 2012 Jiang, Ding, Wang, Yan (bib0004) 2017; 64 Yuan, Wang, Yang, Gui, Ye (bib0013) 2017; 57 Yuan, Ge, Huang, Song (bib0017) 2017; 25 Yuan, Li, Wang (bib0007) 2019 Kadlec, Gabrys, Strandt (bib0006) 2009; 33 Yuan, Zhou, Wang, Yang (bib0020) 2018; 32 Ge, Song, Ding, Huang (bib0009) 2017; 5 Zhou, Zheng, Ge, Song, Shan (bib0003) 2018; 65 Ma, Khatibisepehr, Huang (bib0016) 2014; 61 Khatibisepehr, Huang, Khare (bib0005) 2013; 23 Gonzaga, Meleiro, Kiang, Maciel Filho (bib0019) 2009; 33 Yuan, Ge, Huang, Song, Wang (bib0015) 2017; 13 Jiang, Yan, Huang (bib0010) 2016; 63 Yuan, Ge, Song, Wang, Yang, Zhang (bib0008) 2017; 66 Zhou, Li, Song, Qin (bib0011) 2017; 25 Chen, Dai, Yuan, Gui, Ren, Koivo (bib0021) 2018; 67 Du, Xiong, Wu, Zhang, Zhang, Tao (bib0030) 2016; 47 Yuan, Ge, Song (bib0022) 2014; 53 Rosipal, Trejo (bib0023) 2002; 2 Fortuna, Giannone, Graziani, Xibilia (bib0024) 2007; 56 Yuan, Wang, Yang, Ge, Song, Gui (bib0001) 2018; 65 Gonzaga (10.1016/j.neucom.2018.11.107_bib0019) 2009; 33 Du (10.1016/j.neucom.2018.11.107_bib0030) 2016; 47 Yuan (10.1016/j.neucom.2018.11.107_bib0017) 2017; 25 Yuan (10.1016/j.neucom.2018.11.107_bib0007) 2019 Yu (10.1016/j.neucom.2018.11.107_bib0026) 2018; 65 Yuan (10.1016/j.neucom.2018.11.107_bib0020) 2018; 32 Jiang (10.1016/j.neucom.2018.11.107_bib0010) 2016; 63 Chen (10.1016/j.neucom.2018.11.107_bib0021) 2018; 67 Jiang (10.1016/j.neucom.2018.11.107_bib0004) 2017; 64 Jia (10.1016/j.neucom.2018.11.107_bib0031) 2018 Rosipal (10.1016/j.neucom.2018.11.107_bib0023) 2002; 2 Ge (10.1016/j.neucom.2018.11.107_bib0002) 2017; 12 Fortuna (10.1016/j.neucom.2018.11.107_bib0035) 2007 Ge (10.1016/j.neucom.2018.11.107_bib0009) 2017; 5 Samek (10.1016/j.neucom.2018.11.107_bib0028) 2016; 28 Majumdar (10.1016/j.neucom.2018.11.107_bib0029) 2017 Fortuna (10.1016/j.neucom.2018.11.107_bib0034) 2005; 13 Huang (10.1016/j.neucom.2018.11.107_bib0014) 2012 Bengio (10.1016/j.neucom.2018.11.107_bib0025) 2006 Lee (10.1016/j.neucom.2018.11.107_bib0027) 2016; 13 Yuan (10.1016/j.neucom.2018.11.107_bib0015) 2017; 13 Liu (10.1016/j.neucom.2018.11.107_bib0018) 2013; 23 Fortuna (10.1016/j.neucom.2018.11.107_bib0024) 2007; 56 Yuan (10.1016/j.neucom.2018.11.107_bib0001) 2018; 65 Yuan (10.1016/j.neucom.2018.11.107_bib0032) 2018; 14 Kadlec (10.1016/j.neucom.2018.11.107_bib0006) 2009; 33 Chen (10.1016/j.neucom.2018.11.107_bib0012) 2018; 65 Yuan (10.1016/j.neucom.2018.11.107_bib0013) 2017; 57 Ma (10.1016/j.neucom.2018.11.107_bib0016) 2014; 61 Hauke (10.1016/j.neucom.2018.11.107_bib0033) 2011; 30 Zhou (10.1016/j.neucom.2018.11.107_bib0011) 2017; 25 Yuan (10.1016/j.neucom.2018.11.107_bib0008) 2017; 66 Zhou (10.1016/j.neucom.2018.11.107_bib0003) 2018; 65 Khatibisepehr (10.1016/j.neucom.2018.11.107_bib0005) 2013; 23 Yuan (10.1016/j.neucom.2018.11.107_bib0022) 2014; 53 |
| References_xml | – volume: 33 start-page: 795 year: 2009 end-page: 814 ident: bib0006 article-title: Data-driven soft sensors in the process industry publication-title: Comput. Chem. Eng. – year: 2012 ident: bib0014 article-title: Dynamic modelling and predictive control in solid oxide fuel cells: First principle and data-based approaches – year: 2019 ident: bib0007 article-title: Nonlinear dynamic soft sensor modeling with supervised long short-term memory network publication-title: IEEE. T. Ind. Inf. – year: 2007 ident: bib0035 article-title: Soft Sensors for Monitoring and Control of Industrial Processes – start-page: 1878 year: 2018 end-page: 1887 ident: bib0031 article-title: Stacked denoising tensor auto-encoder for action recognition with spatiotemporal corruptions publication-title: IEEE Trans. Image Process. – volume: 64 start-page: 8148 year: 2017 end-page: 8157 ident: bib0004 article-title: Data-driven distributed local fault detection for large-scale processes based on GA-regularized Canonical Correlation analysis publication-title: IEEE Trans. Ind. Electron. – volume: 65 start-page: 1559 year: 2018 end-page: 1567 ident: bib0012 article-title: Fault detection for non-gaussian processes using generalized canonical correlation analysis and randomized algorithms publication-title: IEEE Trans. Ind. Electron. – volume: 32 start-page: e3040 year: 2018 ident: bib0020 article-title: Multi-similarity measurement driven ensemble just-in-time learning for soft sensing of industrial processes publication-title: J. Chemom. – volume: 14 start-page: 3235 year: 2018 end-page: 3243 ident: bib0032 article-title: Deep learning based feature representation and its application for soft sensor modeling with variable-wise weighted SAE publication-title: IEEE Trans. Ind. Inform. – volume: 23 start-page: 793 year: 2013 end-page: 804 ident: bib0018 article-title: Integrated soft sensor using just-in-time support vector regression and probabilistic analysis for quality prediction of multi-grade processes publication-title: J. Process Control – volume: 65 start-page: 8184 year: 2018 end-page: 8194 ident: bib0003 article-title: Multimode process monitoring based on switching autoregressive dynamic latent variable model publication-title: IEEE Trans. Ind. Electron. – volume: 66 start-page: 837 year: 2017 end-page: 845 ident: bib0008 article-title: Soft sensor modeling of nonlinear industrial processes based on weighted probabilistic projection regression publication-title: IEEE Trans. Instrum. Meas. – volume: 25 start-page: 1124 year: 2017 end-page: 1132 ident: bib0017 article-title: A probabilistic just-in-time learning framework for soft sensor development with missing data publication-title: IEEE Trans. Control Syst. Technol. – volume: 13 start-page: 499 year: 2005 end-page: 508 ident: bib0034 article-title: Soft sensors for product quality monitoring in debutanizer distillation columns publication-title: Control Eng. Pract. – volume: 57 start-page: 15 year: 2017 end-page: 25 ident: bib0013 article-title: Probabilistic density-based regression model for soft sensing of nonlinear industrial processes publication-title: J. Process Control – volume: 23 start-page: 1575 year: 2013 end-page: 1596 ident: bib0005 article-title: Design of inferential sensors in the process industry: a review of Bayesian methods publication-title: J. Process Control – volume: 61 start-page: 518 year: 2014 end-page: 529 ident: bib0016 article-title: A Bayesian framework for real‐time identification of locally weighted partial least squares publication-title: AIChE J. – volume: 2 start-page: 97 year: 2002 end-page: 123 ident: bib0023 article-title: Kernel partial least squares regression in reproducing kernel Hilbert space publication-title: J. Mach. Learn. Res. – volume: 28 start-page: 2660 year: 2016 end-page: 2673 ident: bib0028 article-title: Evaluating the visualization of what a deep neural network has learned publication-title: IEEE Trans. Neural Netw. Learn. Systems. – volume: 53 start-page: 13736 year: 2014 end-page: 13749 ident: bib0022 article-title: Locally weighted kernel principal component regression model for soft sensing of nonlinear time-variant processes publication-title: Ind. Eng. Chem. Res. – volume: 56 start-page: 95 year: 2007 end-page: 101 ident: bib0024 article-title: Virtual instruments based on stacked neural networks to improve product quality monitoring in a refinery publication-title: IEEE Trans. Instrum. Meas. – volume: 30 start-page: 87 year: 2011 end-page: 93 ident: bib0033 article-title: Comparison of values of pearson's and spearman's correlation coefficients on the same sets of data publication-title: Quaest. Geogr. – volume: 13 start-page: 532 year: 2017 end-page: 541 ident: bib0015 article-title: Semisupervised JITL framework for nonlinear industrial soft sensing based on locally semisupervised weighted PCR publication-title: IEEE Trans. Ind. Inform. – volume: 65 start-page: 5060 year: 2018 end-page: 5068 ident: bib0026 article-title: Multi-task autoencoder model for recovering human poses publication-title: IEEE Trans. Ind. Electron. – start-page: 911 year: 2017 end-page: 918 ident: bib0029 article-title: Asymmetric stacked autoencoder publication-title: Proceedings of the International Joint Conference on Neural Networks – volume: 13 start-page: 461 year: 2016 end-page: 472 ident: bib0027 article-title: Oscillometric blood pressure estimation based on deep learning publication-title: IEEE Trans. Ind. Inform. – volume: 67 start-page: 2001 year: 2018 end-page: 2010 ident: bib0021 article-title: Temperature prediction model for roller kiln by ALD-based double locally weighted kernel principal component regression publication-title: IEEE Trans. Instrum. Meas. – volume: 65 start-page: 1508 year: 2018 end-page: 1517 ident: bib0001 article-title: Weighted linear dynamic system for feature representation and soft sensor application in nonlinear dynamic industrial processes publication-title: IEEE Trans. Ind. Electron. – volume: 33 start-page: 43 year: 2009 end-page: 49 ident: bib0019 article-title: ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process publication-title: Comput. Chem. Eng. – start-page: 153 year: 2006 end-page: 160 ident: bib0025 article-title: Greedy layer-wise training of deep networks publication-title: Proceedings of the International Conference on Neural Information Processing Systems – volume: 47 start-page: 1017 year: 2016 end-page: 1027 ident: bib0030 article-title: Stacked convolutional denoising auto-encoders for feature representation publication-title: IEEE Trans. Cybern. – volume: 12 start-page: 310 year: 2017 end-page: 321 ident: bib0002 article-title: Plant-wide industrial process monitoring: a distributed modeling framework publication-title: IEEE Trans. Ind. Inform. – volume: 63 start-page: 377 year: 2016 end-page: 386 ident: bib0010 article-title: Performance-driven distributed PCA process monitoring based on fault-relevant variable selection and bayesian inference publication-title: IEEE Trans. Ind. Electron. – volume: 5 start-page: 20590 year: 2017 end-page: 20616 ident: bib0009 article-title: Data mining and analytics in the process industry: the role of machine learning publication-title: IEEE Access – volume: 25 start-page: 366 year: 2017 end-page: 373 ident: bib0011 article-title: Autoregressive dynamic latent variable models for process monitoring publication-title: IEEE Trans. Control Syst. Technol. – volume: 28 start-page: 2660 year: 2016 ident: 10.1016/j.neucom.2018.11.107_bib0028 article-title: Evaluating the visualization of what a deep neural network has learned publication-title: IEEE Trans. Neural Netw. Learn. Systems. doi: 10.1109/TNNLS.2016.2599820 – start-page: 1878 year: 2018 ident: 10.1016/j.neucom.2018.11.107_bib0031 article-title: Stacked denoising tensor auto-encoder for action recognition with spatiotemporal corruptions publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2017.2781299 – volume: 65 start-page: 8184 year: 2018 ident: 10.1016/j.neucom.2018.11.107_bib0003 article-title: Multimode process monitoring based on switching autoregressive dynamic latent variable model publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2018.2803727 – volume: 25 start-page: 1124 year: 2017 ident: 10.1016/j.neucom.2018.11.107_bib0017 article-title: A probabilistic just-in-time learning framework for soft sensor development with missing data publication-title: IEEE Trans. Control Syst. Technol. doi: 10.1109/TCST.2016.2579609 – volume: 12 start-page: 310 year: 2017 ident: 10.1016/j.neucom.2018.11.107_bib0002 article-title: Plant-wide industrial process monitoring: a distributed modeling framework publication-title: IEEE Trans. Ind. Inform. doi: 10.1109/TII.2015.2509247 – volume: 67 start-page: 2001 year: 2018 ident: 10.1016/j.neucom.2018.11.107_bib0021 article-title: Temperature prediction model for roller kiln by ALD-based double locally weighted kernel principal component regression publication-title: IEEE Trans. Instrum. Meas. doi: 10.1109/TIM.2018.2810678 – volume: 66 start-page: 837 year: 2017 ident: 10.1016/j.neucom.2018.11.107_bib0008 article-title: Soft sensor modeling of nonlinear industrial processes based on weighted probabilistic projection regression publication-title: IEEE Trans. Instrum. Meas. doi: 10.1109/TIM.2017.2658158 – volume: 61 start-page: 518 year: 2014 ident: 10.1016/j.neucom.2018.11.107_bib0016 article-title: A Bayesian framework for real‐time identification of locally weighted partial least squares publication-title: AIChE J. doi: 10.1002/aic.14663 – volume: 65 start-page: 5060 year: 2018 ident: 10.1016/j.neucom.2018.11.107_bib0026 article-title: Multi-task autoencoder model for recovering human poses publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2017.2739691 – volume: 5 start-page: 20590 year: 2017 ident: 10.1016/j.neucom.2018.11.107_bib0009 article-title: Data mining and analytics in the process industry: the role of machine learning publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2756872 – volume: 2 start-page: 97 year: 2002 ident: 10.1016/j.neucom.2018.11.107_bib0023 article-title: Kernel partial least squares regression in reproducing kernel Hilbert space publication-title: J. Mach. Learn. Res. – volume: 47 start-page: 1017 year: 2016 ident: 10.1016/j.neucom.2018.11.107_bib0030 article-title: Stacked convolutional denoising auto-encoders for feature representation publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2016.2536638 – volume: 13 start-page: 532 year: 2017 ident: 10.1016/j.neucom.2018.11.107_bib0015 article-title: Semisupervised JITL framework for nonlinear industrial soft sensing based on locally semisupervised weighted PCR publication-title: IEEE Trans. Ind. Inform. doi: 10.1109/TII.2016.2610839 – volume: 57 start-page: 15 year: 2017 ident: 10.1016/j.neucom.2018.11.107_bib0013 article-title: Probabilistic density-based regression model for soft sensing of nonlinear industrial processes publication-title: J. Process Control doi: 10.1016/j.jprocont.2017.06.002 – start-page: 153 year: 2006 ident: 10.1016/j.neucom.2018.11.107_bib0025 article-title: Greedy layer-wise training of deep networks – volume: 25 start-page: 366 year: 2017 ident: 10.1016/j.neucom.2018.11.107_bib0011 article-title: Autoregressive dynamic latent variable models for process monitoring publication-title: IEEE Trans. Control Syst. Technol. doi: 10.1109/TCST.2016.2550426 – volume: 64 start-page: 8148 year: 2017 ident: 10.1016/j.neucom.2018.11.107_bib0004 article-title: Data-driven distributed local fault detection for large-scale processes based on GA-regularized Canonical Correlation analysis publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2017.2698422 – volume: 14 start-page: 3235 year: 2018 ident: 10.1016/j.neucom.2018.11.107_bib0032 article-title: Deep learning based feature representation and its application for soft sensor modeling with variable-wise weighted SAE publication-title: IEEE Trans. Ind. Inform. doi: 10.1109/TII.2018.2809730 – volume: 23 start-page: 1575 year: 2013 ident: 10.1016/j.neucom.2018.11.107_bib0005 article-title: Design of inferential sensors in the process industry: a review of Bayesian methods publication-title: J. Process Control doi: 10.1016/j.jprocont.2013.05.007 – volume: 13 start-page: 499 year: 2005 ident: 10.1016/j.neucom.2018.11.107_bib0034 article-title: Soft sensors for product quality monitoring in debutanizer distillation columns publication-title: Control Eng. Pract. doi: 10.1016/j.conengprac.2004.04.013 – volume: 33 start-page: 795 year: 2009 ident: 10.1016/j.neucom.2018.11.107_bib0006 article-title: Data-driven soft sensors in the process industry publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2008.12.012 – volume: 13 start-page: 461 year: 2016 ident: 10.1016/j.neucom.2018.11.107_bib0027 article-title: Oscillometric blood pressure estimation based on deep learning publication-title: IEEE Trans. Ind. Inform. doi: 10.1109/TII.2016.2612640 – volume: 33 start-page: 43 year: 2009 ident: 10.1016/j.neucom.2018.11.107_bib0019 article-title: ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process publication-title: Comput. Chem. Eng. doi: 10.1016/j.compchemeng.2008.05.019 – volume: 65 start-page: 1559 year: 2018 ident: 10.1016/j.neucom.2018.11.107_bib0012 article-title: Fault detection for non-gaussian processes using generalized canonical correlation analysis and randomized algorithms publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2017.2733501 – year: 2012 ident: 10.1016/j.neucom.2018.11.107_bib0014 – year: 2019 ident: 10.1016/j.neucom.2018.11.107_bib0007 article-title: Nonlinear dynamic soft sensor modeling with supervised long short-term memory network publication-title: IEEE. T. Ind. Inf. – volume: 32 start-page: e3040 year: 2018 ident: 10.1016/j.neucom.2018.11.107_bib0020 article-title: Multi-similarity measurement driven ensemble just-in-time learning for soft sensing of industrial processes publication-title: J. Chemom. doi: 10.1002/cem.3040 – year: 2007 ident: 10.1016/j.neucom.2018.11.107_bib0035 – volume: 63 start-page: 377 year: 2016 ident: 10.1016/j.neucom.2018.11.107_bib0010 article-title: Performance-driven distributed PCA process monitoring based on fault-relevant variable selection and bayesian inference publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2015.2466557 – volume: 53 start-page: 13736 year: 2014 ident: 10.1016/j.neucom.2018.11.107_bib0022 article-title: Locally weighted kernel principal component regression model for soft sensing of nonlinear time-variant processes publication-title: Ind. Eng. Chem. Res. doi: 10.1021/ie4041252 – volume: 65 start-page: 1508 year: 2018 ident: 10.1016/j.neucom.2018.11.107_bib0001 article-title: Weighted linear dynamic system for feature representation and soft sensor application in nonlinear dynamic industrial processes publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2017.2733443 – start-page: 911 year: 2017 ident: 10.1016/j.neucom.2018.11.107_bib0029 article-title: Asymmetric stacked autoencoder – volume: 30 start-page: 87 year: 2011 ident: 10.1016/j.neucom.2018.11.107_bib0033 article-title: Comparison of values of pearson's and spearman's correlation coefficients on the same sets of data publication-title: Quaest. Geogr. – volume: 23 start-page: 793 year: 2013 ident: 10.1016/j.neucom.2018.11.107_bib0018 article-title: Integrated soft sensor using just-in-time support vector regression and probabilistic analysis for quality prediction of multi-grade processes publication-title: J. Process Control doi: 10.1016/j.jprocont.2013.03.008 – volume: 56 start-page: 95 year: 2007 ident: 10.1016/j.neucom.2018.11.107_bib0024 article-title: Virtual instruments based on stacked neural networks to improve product quality monitoring in a refinery publication-title: IEEE Trans. Instrum. Meas. doi: 10.1109/TIM.2006.887331 |
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