Fault diagnosis of a mixing process using deep qualitative knowledge representation of physical behaviour

1990 ◽  
Vol 3 (2) ◽  
Author(s):  
J. Zhang ◽  
P.D. Roberts ◽  
J.E. Ellis
Keyword(s):  
Fault Diagnosis ◽  
Knowledge Representation ◽  
Mixing Process ◽  
Physical Behaviour ◽  
Qualitative Knowledge

Design of Fault Diagnosis Expert System of Transformer

2013 ◽  
Vol 291-294 ◽  
pp. 2557-2561
Author(s):  
Tao Sun ◽  
Hai Bo Liu
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Knowledge Representation ◽  
System Design ◽  
System Of Systems ◽  
Knowledge Representation And Reasoning ◽  
Design Knowledge ◽  
Systems Architecture ◽  
Fault Diagnosis System ◽  
Transformer Fault

The transformer fault diagnosis expert system design knowledge representation and reasoning mechanisms are the key issue. Characteristics of transformer fault diagnosis system based on human experts, learning on the basis of the human expert diagnosis of transformer faults, to build a transformer fault diagnosis expert system of systems architecture, knowledge representation and reasoning mechanisms for a more detailed analysis and discussion.

scholarly journals A qualitative knowledge representation model and application for crisis events

2018 ◽  
Vol 126 ◽  
pp. 1828-1836
Author(s):  
Jiangnan Qiu ◽  
Min Zuo ◽  
Shuning Yang ◽  
Huayan Shi
Keyword(s):  
Knowledge Representation ◽  
Representation Model ◽  
Crisis Events ◽  
Qualitative Knowledge

An Expert System Shell for Fault Diagnosis

Robotica ◽  
2001 ◽  
Vol 19 (6) ◽  
pp. 669-674 ◽  
Author(s):  
Jie Yang ◽  
Chenzhou Ye ◽  
Xiaoli Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Expert System ◽  
Knowledge Representation ◽  
Expert Systems ◽  
Knowledge Representation And Reasoning ◽  
Multiple Models ◽  
Object Model ◽  
Knowledge Models ◽  
Expert System Shell

Traditional expert systems for fault diagnosis have a bottleneck in knowledge acquisition, and have limitations in knowledge representation and reasoning. A new expert system shell for fault diagnosis is presented in this paper to develop multiple knowledge models (object model, rules, neural network, case-base and diagnose models) hierarchically based on multiple knowledge. The structure of the expert system shell and the knowledge representation of multiple models are described. Diagnostic algorithms are presented for automatic modeling and hierarchical reasoning. It will be shown that the expert system shell is very effective in building diagnostic expert systems.

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