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Fault diagnosis based on signed digraph combined with dynamic kernel PLS and SVR / Ning Lu in Industrial & engineering chemistry research, Vol. 47 N° 23 (Décembre 2008) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 47 N° 23 (Décembre 2008) . - p. 9447–9456 Titre : Fault diagnosis based on signed digraph combined with dynamic kernel PLS and SVR Type de document : texte imprimé Auteurs : Ning Lu, Auteur ; Xiong Wang, Auteur Année de publication : 2009 Article en page(s) : p. 9447–9456 Note générale : Chemistry engineering Langues : Anglais (eng) Mots-clés : Signed  digraph  Dynamic  kernel  PLS  and  SVR Résumé : The signed digraph (SDG) method for fault diagnosis, which is one of the model-based methods, has been widely applied in the chemical industry in recent years. However, how to elicit appropriate thresholds for SDG is a very difficult problem. This study presents a new hybrid method combining SDG with dynamic kernel partial least-squares (DKPLS) and support vector regression (SVR) for fault diagnosis. Using the relationships of each variable in SDG, a series of DKPLS-SVR models are built to estimate the values of the measured variables in process. The difference between the estimation and the measured value can determine the qualitative status of the variable, and then the fault can be diagnosed by SDG reasoning. Therefore, the threshold of each measured variable does not need to be decided in advance. The method can also overcome the limited availability of using the KPLS method alone in identifying the root cause. To verify the performance of the proposed method, its application is illustrated on the Tennessee Eastman (TE) challenge process. Through case studies, the proposed method demonstrates a good diagnosis capability compared with previous hybrid methods. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8009457 [article] Fault diagnosis based on signed digraph combined with dynamic kernel PLS and SVR [texte imprimé] / Ning Lu, Auteur ; Xiong Wang, Auteur . - 2009 . - p. 9447–9456. Chemistry engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 N° 23 (Décembre 2008) . - p. 9447–9456 Mots-clés : Signed  digraph  Dynamic  kernel  PLS  and  SVR Résumé : The signed digraph (SDG) method for fault diagnosis, which is one of the model-based methods, has been widely applied in the chemical industry in recent years. However, how to elicit appropriate thresholds for SDG is a very difficult problem. This study presents a new hybrid method combining SDG with dynamic kernel partial least-squares (DKPLS) and support vector regression (SVR) for fault diagnosis. Using the relationships of each variable in SDG, a series of DKPLS-SVR models are built to estimate the values of the measured variables in process. The difference between the estimation and the measured value can determine the qualitative status of the variable, and then the fault can be diagnosed by SDG reasoning. Therefore, the threshold of each measured variable does not need to be decided in advance. The method can also overcome the limited availability of using the KPLS method alone in identifying the root cause. To verify the performance of the proposed method, its application is illustrated on the Tennessee Eastman (TE) challenge process. Through case studies, the proposed method demonstrates a good diagnosis capability compared with previous hybrid methods. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8009457

Integrated framework of probabilistic signed digraph based fault diagnosis approach to a gas fractionation unit / Ning Lu in Industrial & engineering chemistry research, Vol. 50 N° 17 (Septembre 2011) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 50 N° 17 (Septembre 2011) . - pp. 10062–10073 Titre : Integrated framework of probabilistic signed digraph based fault diagnosis approach to a gas fractionation unit Type de document : texte imprimé Auteurs : Ning Lu, Auteur ; Zhihua Xiong, Auteur ; Xiong Wang, Auteur Année de publication : 2011 Article en page(s) : pp. 10062–10073 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Probabilistic  signed  digraph  Gas  fractionation  unit Résumé : An integrated implementation solution and theoretical framework of fault diagnosis approach based on probabilistic signed digraph (PSDG) is proposed and applied to a gas fractionation unit. On the basis of the primary method of PSDG presented in our previous work, a complete framework of PSDG is constructed, including its definition and reasoning to its implementation. Nodes and branches in PSDG contain uncertain information and their a priori conditional probabilistic parameters are decided by using little knowledge of the studied plant; thus, a PSDG model can be built properly. After cycle processing and model simplification, PSDG reasoning can be conducted approximately on the basis of consistent rule. In implementation of PSDG, the probabilities of candidate faults can be computed and arranged, and the most possible fault is found. Therefore the real fault cause can be further confirmed reasonably. Compared with the conventional qualitative SDG, the qualitative ambiguities in PSDG can be reduced to some extent. The proposed method is applied to a gas fractionation unit, and experimental results on real operation data show the validity and advantages of the PSDG framework. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie200016t [article] Integrated framework of probabilistic signed digraph based fault diagnosis approach to a gas fractionation unit [texte imprimé] / Ning Lu, Auteur ; Zhihua Xiong, Auteur ; Xiong Wang, Auteur . - 2011 . - pp. 10062–10073. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 17 (Septembre 2011) . - pp. 10062–10073 Mots-clés : Probabilistic  signed  digraph  Gas  fractionation  unit Résumé : An integrated implementation solution and theoretical framework of fault diagnosis approach based on probabilistic signed digraph (PSDG) is proposed and applied to a gas fractionation unit. On the basis of the primary method of PSDG presented in our previous work, a complete framework of PSDG is constructed, including its definition and reasoning to its implementation. Nodes and branches in PSDG contain uncertain information and their a priori conditional probabilistic parameters are decided by using little knowledge of the studied plant; thus, a PSDG model can be built properly. After cycle processing and model simplification, PSDG reasoning can be conducted approximately on the basis of consistent rule. In implementation of PSDG, the probabilities of candidate faults can be computed and arranged, and the most possible fault is found. Therefore the real fault cause can be further confirmed reasonably. Compared with the conventional qualitative SDG, the qualitative ambiguities in PSDG can be reduced to some extent. The proposed method is applied to a gas fractionation unit, and experimental results on real operation data show the validity and advantages of the PSDG framework. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie200016t