Documents disponibles écrits par cet auteur

Data-driven soft sensor design with multiple-rate sampled data / Bao, Lin in Industrial & engineering chemistry research, Vol. 48 N° 11 (Juin 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 11 (Juin 2009) . - pp. 5379–5387 Titre : Data-driven soft sensor design with multiple-rate sampled data : a comparative study Type de document : texte imprimé Auteurs : Bao, Lin, Auteur ; Bodil Recke, Auteur ; Torben M. Schmidt, Auteur Année de publication : 2009 Article en page(s) : pp. 5379–5387 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Multirate  systems  Samples  Numerical  interpolation  Polynomial  transformation  Data  lifting  Weighted  partial  least  squares Résumé : Multirate systems are common in industrial processes where quality measurements have slower sampling rates than other process variables. Since intersample information is desirable for effective quality control, different approaches have been reported to estimate the quality between samples, including the numerical interpolation, polynomial transformation, data lifting, and weighted partial least squares (WPLS). Two modifications to the original data lifting approach are proposed in this paper: reformulating the extraction of a fast model as an optimization problem and ensuring the desired model properties through Tikhonov Regularization. A comparative investigation of the four approaches is performed. Their applicability, accuracy, and robustness to process noise are evaluated with a single-input single-output (SISO) system. The modified data lifting and WPLS approaches are implemented to design quality soft sensors for cement kiln processes using data collected from a simulator and a plant log system. Preliminary results reveal that the WPLS approach is able to provide accurate one-step-ahead prediction. The regularized data lifting technique predicts the product quality of cement kiln systems reasonably well, demonstrating the potential to be used for effective quality control and as an advanced component of process analytical technology (PAT). En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801084e [article] Data-driven soft sensor design with multiple-rate sampled data : a comparative study [texte imprimé] / Bao, Lin, Auteur ; Bodil Recke, Auteur ; Torben M. Schmidt, Auteur . - 2009 . - pp. 5379–5387. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 11 (Juin 2009) . - pp. 5379–5387 Mots-clés : Multirate  systems  Samples  Numerical  interpolation  Polynomial  transformation  Data  lifting  Weighted  partial  least  squares Résumé : Multirate systems are common in industrial processes where quality measurements have slower sampling rates than other process variables. Since intersample information is desirable for effective quality control, different approaches have been reported to estimate the quality between samples, including the numerical interpolation, polynomial transformation, data lifting, and weighted partial least squares (WPLS). Two modifications to the original data lifting approach are proposed in this paper: reformulating the extraction of a fast model as an optimization problem and ensuring the desired model properties through Tikhonov Regularization. A comparative investigation of the four approaches is performed. Their applicability, accuracy, and robustness to process noise are evaluated with a single-input single-output (SISO) system. The modified data lifting and WPLS approaches are implemented to design quality soft sensors for cement kiln processes using data collected from a simulator and a plant log system. Preliminary results reveal that the WPLS approach is able to provide accurate one-step-ahead prediction. The regularized data lifting technique predicts the product quality of cement kiln systems reasonably well, demonstrating the potential to be used for effective quality control and as an advanced component of process analytical technology (PAT). En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801084e

Observer and data-driven-model-based fault detection in power plant coal mills / Odgaard, P.F. in IEEE transactions on energy conversion, Vol. 23 n°2 (Juin 2008) 

Public ISBD [article]  in IEEE transactions on energy conversion > Vol. 23 n°2 (Juin 2008) . - pp. 659 - 668 Titre : Observer and data-driven-model-based fault detection in power plant coal mills Type de document : texte imprimé Auteurs : Odgaard, P.F., Auteur ; Bao, Lin, Auteur ; Jorgensen, S.B., Auteur Année de publication : 2008 Article en page(s) : pp. 659 - 668 Note générale : Energy conversion Langues : Anglais (eng) Mots-clés : Fault  location;  least  mean  squares  methods;  power  plants;  principal  component  analysis Résumé : This paper presents and compares model-based and data-driven fault detection approaches for coal mill systems. The first approach detects faults with an optimal unknown input observer developed from a simplified energy balance model. Due to the time-consuming effort in developing a first principles model with motor power as the controlled variable, data-driven methods for fault detection are also investigated. Regression models that represent normal operating conditions (NOCs) are developed with both static and dynamic principal component analysis and partial least squares methods. The residual between process measurement and the NOC model prediction is used for fault detection. A hybrid approach, where a data-driven model is employed to derive an optimal unknown input observer, is also implemented. The three methods are evaluated with case studies on coal mill data, which includes a fault caused by a blocked inlet pipe. All three approaches detect the fault as it emerges. The optimal unknown input observer approach is most robust, in that, it has no false positives. On the other hand, the data-driven approaches are more straightforward to implement, since they just require the selection of appropriate confidence limit to avoid false detection. The proposed hybrid approach is promising for systems where a first principles model is cumbersome to obtain. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4470327&sortType%3Das [...] [article] Observer and data-driven-model-based fault detection in power plant coal mills [texte imprimé] / Odgaard, P.F., Auteur ; Bao, Lin, Auteur ; Jorgensen, S.B., Auteur . - 2008 . - pp. 659 - 668. Energy conversion Langues : Anglais (eng) in IEEE transactions on energy conversion > Vol. 23 n°2 (Juin 2008) . - pp. 659 - 668 Mots-clés : Fault  location;  least  mean  squares  methods;  power  plants;  principal  component  analysis Résumé : This paper presents and compares model-based and data-driven fault detection approaches for coal mill systems. The first approach detects faults with an optimal unknown input observer developed from a simplified energy balance model. Due to the time-consuming effort in developing a first principles model with motor power as the controlled variable, data-driven methods for fault detection are also investigated. Regression models that represent normal operating conditions (NOCs) are developed with both static and dynamic principal component analysis and partial least squares methods. The residual between process measurement and the NOC model prediction is used for fault detection. A hybrid approach, where a data-driven model is employed to derive an optimal unknown input observer, is also implemented. The three methods are evaluated with case studies on coal mill data, which includes a fault caused by a blocked inlet pipe. All three approaches detect the fault as it emerges. The optimal unknown input observer approach is most robust, in that, it has no false positives. On the other hand, the data-driven approaches are more straightforward to implement, since they just require the selection of appropriate confidence limit to avoid false detection. The proposed hybrid approach is promising for systems where a first principles model is cumbersome to obtain. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4470327&sortType%3Das [...]