DocumentCode :
1792775
Title :
Trend-weighted rule-based expert system for process diagnosis
Author :
Curvelo de Souza, Danilo ; Doria Neto, Adriao Duarte ; Guedes, Luiz Affonso
Author_Institution :
Dept. of Comput. Eng., Fed. Univ. of Rio Grande do Norte, Natal, Brazil
fYear :
2014
fDate :
16-19 Sept. 2014
Firstpage :
1
Lastpage :
4
Abstract :
This paper presents and innovative technique-referred to as trend-weighted rule-based expert system (TWRBES) - grounded in the integration of two existing tools of the artificial intelligence field, expert systems (ES) and qualitative trend analysis (QTA). The main goal of this approach is to benefit of the main advantages associated with each of the techniques used, such as the ability to represent knowledge through rules and the ability to extract the behavior and the trends of a continuous signal. Such integration allows a direct purpose in industrial environment applications, especially in the intelligent automation field. This paper introduces this technique and preliminary results obtained from applying it to industrial process diagnosis.
Keywords :
artificial intelligence; expert systems; failure analysis; fault diagnosis; knowledge representation; production engineering computing; QTA; TWRBES; artificial intelligence; industrial process diagnosis; knowledge representation; qualitative trend analysis; trend-weighted rule-based expert system; Accuracy; Expert systems; Market research; Monitoring; Polynomials; diagnosis; expert system; intelligent automation; qualitative trend analysis; rules;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Emerging Technology and Factory Automation (ETFA), 2014 IEEE
Conference_Location :
Barcelona
Type :
conf
DOI :
10.1109/ETFA.2014.7005325
Filename :
7005325
Link To Document :
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