Real-time Diagnosis System Development of Common Rail Diesel Based on Expert System

2005 ◽  
Author(s):  
Guomin Song ◽  
Fuyuan Yang ◽  
Minggao Ouyang ◽  
Jun Li ◽  
Linfeng Hu
2011 ◽  
Vol 383-390 ◽  
pp. 1536-1541
Author(s):  
Huai Bin Zhang ◽  
Hua Yang

According to the lack of simple, backward and low precision in fault diagnosis on hydraulic power cart for aircraft, an efficient vehicle fault diagnosis system on hydraulic power cart for aircraft is developed based on embedded technology. This system can identify the cause of the faults quickly and accurately according to the data collected in the spot and real-time analysis using expert system, the results show it greatly improves the efficiency and accuracy of fault diagnosis on hydraulic power cart for aircraft.


2021 ◽  
Vol 23 (4) ◽  
pp. 57-62 ◽  
Author(s):  
Amjad Rehman ◽  
Tariq Sadad ◽  
Tanzila Saba ◽  
Ayyaz Hussain ◽  
Usman Tariq

1984 ◽  
Vol 1 (4) ◽  
pp. 18-29 ◽  
Author(s):  
Jean-Pierre Laurent ◽  
Jacqueline Ayel ◽  
Franck Thome ◽  
Danielle Ziebelin

AbstractThe purpose of this paper is to evaluate and compare three of the most powerful expert system tools available: KEE from Intellicorp, Knowledge Craft from The Carnegie Group Inc., and ART from Inference Corporation.These three tools are industrial development environments which are fully supported and well beyond research prototypes. They were implemented on Lisp machines initially, but will soon be available on conventional computers. The three systems are very flexible and offer many ways of representing knowledge.The first part of the paper is a technical overview of each tool. The second part presents their common features. The third part discusses the advantages and drawbacks of each, according to types of application.This work has been partially sponsored by the Commission of the European communities within the Esprit Project 932 entitled ‘Knowledged-based Real-Time supervision in Computer Integrated Manufacturing’. We are grateful to Didier Gaget-Soufflot (Graphael, France) and Bernard Raust (CGE/TITN) for their participation in the evaluation of KEE and ART respectivily.


2017 ◽  
Author(s):  
Andressa dos S. Nicolau ◽  
João P. da S. C. Augusto ◽  
Roberto Schirru

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