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Rough set-based fuzzy rule acquisition and Its application for fault diagnosis in petrochemical process / Zhiqiang Geng in Industrial & engineering chemistry research, Vol. 48 N°2 (Janvier 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N°2 (Janvier 2009) . - p 827–836 Titre : Rough set-based fuzzy rule acquisition and Its application for fault diagnosis in petrochemical process Type de document : texte imprimé Auteurs : Zhiqiang Geng, Auteur ; Qunxiong Zhu, Auteur Année de publication : 2009 Article en page(s) : p 827–836 Note générale : chemical engineering Langues : Anglais (eng) Mots-clés : Petrochemical  Process Résumé : Data mining techniques can discover experience, knowledge, and operational rules from a large industrial data set to recognize process abnormal situations or faults, further improve production-level, and optimize operational conditions. In this paper, a rough set-based fuzzy rule acquisition approach and a fault diagnosis scheme of industrial process are studied in detail. A new heuristic reduct algorithm is proposed to obtain the optimum reduction set of decision information system. Moreover, a fuzzy discretization model for continuous data based on normal distribution of process variables is put forward to overcome the subjective of selecting fuzzy membership functions and decrease the sensitivity to noise signals. Furthermore, the proposed data mining algorithm and fault diagnosis scheme are applied into a petrochemical process. The validity of the proposed strategy is verified by application of a practical ethylene cracking furnace system, which can discover abnormal process situations and improve plant safety in petrochemical industry. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie071171g [article] Rough set-based fuzzy rule acquisition and Its application for fault diagnosis in petrochemical process [texte imprimé] / Zhiqiang Geng, Auteur ; Qunxiong Zhu, Auteur . - 2009 . - p 827–836. chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N°2 (Janvier 2009) . - p 827–836 Mots-clés : Petrochemical  Process Résumé : Data mining techniques can discover experience, knowledge, and operational rules from a large industrial data set to recognize process abnormal situations or faults, further improve production-level, and optimize operational conditions. In this paper, a rough set-based fuzzy rule acquisition approach and a fault diagnosis scheme of industrial process are studied in detail. A new heuristic reduct algorithm is proposed to obtain the optimum reduction set of decision information system. Moreover, a fuzzy discretization model for continuous data based on normal distribution of process variables is put forward to overcome the subjective of selecting fuzzy membership functions and decrease the sensitivity to noise signals. Furthermore, the proposed data mining algorithm and fault diagnosis scheme are applied into a petrochemical process. The validity of the proposed strategy is verified by application of a practical ethylene cracking furnace system, which can discover abnormal process situations and improve plant safety in petrochemical industry. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie071171g