An Object Frame Knowledge Representation Approach for Fault Diagnosis Expert System

2011 ◽  
Author(s):  
Bailin Liu ◽  
Mingye Duan ◽  
Gang Zhao
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Knowledge Representation ◽  
Object Frame

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.

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.

Key Technologies Study for Automobile Fault Diagnosis Expert System

2012 ◽  
Vol 457-458 ◽  
pp. 913-920
Author(s):  
Li Li Zhang ◽  
Jiang Wei Chu
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Knowledge Representation ◽  
Intelligent System ◽  
New Technology ◽  
Large Collection ◽  
Inference Method ◽  
Development Direction ◽  
Key Technologies ◽  
Inference Strategy

Expert system for automobile fault diagnosis is an intelligent system, and its key technologies are knowledge acquisition, knowledge representation and inference strategy. Based on large collection of papers which on abroad or on home, some resolved methods have been presented in this paper, including improving of traditional method such as automotive acquisition of fault rules, combined knowledge representation and inference method diversification, applying of new theory or new technology such as case-base expert system, fuzzy-base expert system, neural network-base expert system and action-base expert system and so on. And then put forward to that intellectualization, cyberization and integration are the future development direction of automobile fault diagnosis expert system.

A Fault Diagnosis Expert System for Building Chillers

2017 ◽  
Vol 11 (4) ◽  
pp. 270
Author(s):  
C. N. Tan ◽  
C. F. Tan ◽  
M. A. Abdullah
Keyword(s):  
Fault Diagnosis ◽  
Expert System

Application of expert system techniques to fault diagnosis

1984 ◽  
Vol 29 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Hiromitsu Kumamoto ◽  
Kenji Ikenchi ◽  
Koichi Inoue ◽  
Ernest J. Henley
Keyword(s):  
Fault Diagnosis ◽  
Expert System

General Fault Monitoring and Diagnosis Expert System Based on Fault Tree and Multi-Sensor Information

2011 ◽  
Vol 121-126 ◽  
pp. 4481-4485
Author(s):  
Ai Yu Zhang ◽  
Xiao Guang Zhao ◽  
Lei Zhang
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Expert Knowledge ◽  
Sensor Failure ◽  
Fault Trees ◽  
Intelligent Algorithms ◽  
Monitoring And Diagnosis ◽  
Backward Reasoning ◽  
Fault Monitoring ◽  
Sensor Information

Due to the limited generality of traditional fault diagnosis expert system and its low accuracy of extracting failure symptoms, a general fault monitoring and diagnosis expert system has been built. For different devices, users can build fault trees in an interactive way and then the fault trees will be saved as expert knowledge. A variety of sensors are fixed to monitor the real-time condition of the device and intelligent algorithms such as wavelet transform and neural network are used to assist the extraction of failure symptoms. On the basis of integration of multi-sensor failure symptoms, the fault diagnosis is realized through forward and backward reasoning. The simulation diagnosis experiments of NC device have shown the effectiveness of the proposed method.

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