Implementing an expert system for fault diagnosis of electronic equipment

1995 ◽  
Vol 8 (3) ◽  
pp. 355-364 ◽  
Author(s):  
T. Satyanarayana ◽  
G. Subramanyam ◽  
K.V. Rama Rao
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Electronic Equipment

Fault Diagnosis Expert System for Special Electronic Equipment Based on the Fuzzy Neural Network

2012 ◽  
Vol 241-244 ◽  
pp. 401-404
Author(s):  
Xue Zhong Yin ◽  
Jie Gui Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Expert System ◽  
Fuzzy Neural Network ◽  
Electronic System ◽  
Electronic Equipment ◽  
Diagnostic Model ◽  
Model Based ◽  
Proposed Model ◽  
Fuzzy Neural

In order to improve the efficiency and reliability of fault diagnosis for the special electronic equipment, an intelligent fault diagnostic model based on Fuzzy Neural Network (FNN) is proposed. Firstly, the fault diagnosis model based on the FNN Expert System (ES) is built. Secondly, the fault diagnosis expert system of the special electronic equipment based on this model is introduced. Finally, experiments show that the proposed model is correct and the FD system is effective. Moreover, the given method provides a new way of fault diagnosis for other modern electronic system.

Research on Electronic Equipment Fault Diagnosis Expert System Based on Embedded Linux

2013 ◽  
Vol 683 ◽  
pp. 837-840
Author(s):  
Jian Hu Zhang ◽  
Lei Lei ◽  
Jia Feng Li ◽  
Xin You Cui ◽  
Yong Wu
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Embedded Linux ◽  
Electronic Equipment ◽  
Accuracy Rate ◽  
Embedded Linux System ◽  
Knowledge Updating ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Circuit Fault Diagnosis

This paper elaborate one circuit fault diagnosis method about electronic equipment circuit detection combined with expert system on ARM9 and embedded Linux platform and design CLIPS expert system using DSP combined with CPLD data acquisition, making full use of DSP high for-speed data processing capability and then passing the data to the Embedded Linux system operation. Expert system implemente a real-time fault diagnosis according to the the predefined fault diagnosis Knowledge. Compared with traditional testing equipment, the expert system has the advantage of knowledge updating conveniently, high fault diagnosis accuracy rate, etc.

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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