Fault Knowledge Acquisition of Electronic Equipment

2013 ◽  
Vol 397-400 ◽  
pp. 1145-1147
Author(s):  
Feng Hua Zou ◽  
Hong Zhang ◽  
Lu Wang ◽  
Dan Li
Keyword(s):  
Fault Diagnosis ◽  
Data Collection ◽  
Knowledge Acquisition ◽  
Fault Location ◽  
Original Data ◽  
Electronic Equipment ◽  
New Method ◽  
Difficult Situation ◽  
Diagnosis System ◽  
Intelligent Diagnosis

The difficulties of acquiring fault knowledge severely handicap the development of intelligent diagnosis system (IDS) of military electronic equipment (MEE) in our country.For MEE fault diagnosis of fault original data collection difficult situation, a new method is presented,Which auto-acquires fault knowledge by simulating all possible faults of equipment.The approach presented in this paper makes the work of KA engineer easier, and makes fast diagnosis fault location and fault reasons possible.

Research on Intelligent Fault Diagnosis Methods of Armored Vehicles Electrical System

2013 ◽  
Vol 753-755 ◽  
pp. 2175-2178
Author(s):  
Lu Wang ◽  
Gui You Lu ◽  
Dan Li ◽  
Guo Bao Ding
Keyword(s):  
Fault Diagnosis ◽  
Knowledge Acquisition ◽  
Fault Location ◽  
Fault Simulation ◽  
Original Data ◽  
Electrical System ◽  
Difficult Situation ◽  
Intelligent Computing ◽  
Armored Vehicles ◽  
Logic Reasoning

For armored vehicles electrical system fault diagnosis of fault original data collection difficult situation, a new intelligent computing programs designed based on the fuzzy set theory and possibility distribution theory and fuzzy logic reasoning design, which realized the process of KA automatization through the combination of fault simulation technology and knowledge acquisition technology. The approach presented in this paper makes the work of knowledge acquisition (KA) engineer easier, and makes fast diagnosis fault location and fault reasons possible.

Study on Remote Control and Fault Diagnosis for Ultrahigh Speed Grinding

2007 ◽  
Vol 359-360 ◽  
pp. 518-522
Author(s):  
Wan Shan Wang ◽  
Tian Biao Yu ◽  
Xing Yu Jiang ◽  
Jian Yu Yang
Keyword(s):  
Remote Control ◽  
Fault Diagnosis ◽  
Knowledge Acquisition ◽  
Rough Set ◽  
Theoretical Research ◽  
Reduction Rule ◽  
Diagnosis System ◽  
Linear Speed ◽  
Fault Diagnosis System ◽  
Diagnosis Model

Remote control and fault diagnosis of ultrahigh speeding grinding is studied, which is based on the theory of rough set. Knowledge acquisition and reduction rule of fault diagnosis, realization method of remote control for ultrahigh speed grinding are studied, diagnosis model is established. Based on the theoretical research and ultrahigh speed grinder with a linear speed of 250 m/s, the remote control and fault diagnosis system of ultrahigh speed grinding is developed. Results of the system running show that the environment is improved, the mental pressure of workers is relieved and the efficiency is improved. At the same time, it proves that the ability to diagnosis and the accuracy of diagnosis for the ultrahigh speed grinding are improved and the time for diagnosis is shortened by applying rough set.

New Method of Fault Knowledge Acquisition of Electronic Equipment

2014 ◽  
Vol 496-500 ◽  
pp. 931-934
Author(s):  
Zhi Cheng Huo ◽  
Qi Shun Sun ◽  
Feng Jun Qi ◽  
Guo Bao Ding
Keyword(s):  
Knowledge Acquisition ◽  
Electronic Equipment ◽  
New Method ◽  
Topological Information ◽  
Amplifier Circuit ◽  
C Language ◽  
Transistor Amplifier ◽  
Analog System ◽  
Non Linear ◽  
Electric Equipment

For the problems like discreteness, tolerance, non-linear of the parts, acquiring the fault knowledge of analog system in electric equipment is hard. This method realized the process of KA automatization through the combination of PSPICE software and C language and taking command lines as combining site. Using the batch file, the programs will form some topological information and parameter information about the fault states of a circuit system each time. The result of a experiment about an Basic Transistor Amplifier circuit proves its feasibility.

Mine Fan Fault Diagnosis Based on EMD and SVM

2011 ◽  
Vol 63-64 ◽  
pp. 449-452 ◽  
Author(s):  
Jun Fa Leng ◽  
Shuang Xi Jing
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Pattern Classification ◽  
Diagnostic System ◽  
New Method ◽  
Intelligent Diagnosis ◽  
Linear Pattern ◽  
Non Linear ◽  
On Line ◽  
Integrative Method

In this research, a new method based on EMD and SVM for mine fan fault diagnosis is introduced. With EMD, fault feature can be extracted quickly and accurately, and taken as the input samples for SVM with the outstanding non-linear pattern classification performances. 5 two-class SVM classifiers are designed in order to classify and diagnosis 5 typical fault patterns of mine fan. The result of this research shows that the integrative method of EMD and SVM is very fit for the intelligent diagnosis and fault patterns recognition, and it will lead to the possible development of an automated and on-line mine fan condition monitoring and diagnostic system.

Power Transformer Fault Diagnosis System Based on Learn++

2014 ◽  
Vol 602-605 ◽  
pp. 2053-2056
Author(s):  
Bin Chen ◽  
Bo Meng
Keyword(s):  
Fault Diagnosis ◽  
Incremental Learning ◽  
Traditional Method ◽  
Power Transformer ◽  
New Method ◽  
Diagnosis System ◽  
Learning Machines ◽  
Fault Diagnosis System ◽  
Transformer Fault

Aiming at the shortages of traditional method for power transformer fault diagnosis, the ensemble idea and incremental learning idea are used for better performance. The SVM is selected to establish the fault diagnosis models as sub learning machines. And then, the Learn++ algorithm is used to aggregate the sub learning machines. The new with new method will ensure the accuracy of fault diagnosis, and will update online. The experiments demonstrate that the performance of power transformer fault diagnosis system based on Learn++ is the best.

The Remote Fault Intelligent Diagnosis System Based on B/S Structure

2011 ◽  
Vol 328-330 ◽  
pp. 1067-1071
Author(s):  
Jia Liu ◽  
Chun Liang Zhang ◽  
Jian Li ◽  
Sen Li ◽  
Yue Hua Xiong
Keyword(s):  
Fault Diagnosis ◽  
Markov Models ◽  
Support Vector ◽  
Main Function ◽  
Test Bed ◽  
Diagnosis System ◽  
Intelligent Diagnosis ◽  
Database Technology ◽  
Fault Diagnosis System ◽  
Remote Fault Diagnosis

The feasibility and superiority of the remote fault diagnosis system based on B/S structure is analyzed in this paper. The B/S structure is introduced and compared with C/S structure briefly. The paper summarize frame and main function module of the remote fault diagnosis system and introduce its key technology, such as data acquisition technology, data transmission technology between server and client, intelligent diagnosis technology, database technology etc. The hybrid model of support vector machine (SVM) and hidden markov models(HMM) is used as a intelligent diagnosis method of the system, and a new design which could improve the integrity and privacy of the system database data is applied. According to the diagnostic results to all kinds of simulated faults in the Bently vibration test bed, it shows the system is not only stable, reliable and high accuracy, but also has a certain application value to engineering.

Research on Fault Diagnosis of Weapon Equipment Based on Support Vector Machines

2012 ◽  
Vol 466-467 ◽  
pp. 1242-1245 ◽  
Author(s):  
Lin Zhang ◽  
Tao Liu ◽  
An Tang Zhang ◽  
Peng Xu ◽  
Ke Lian
Keyword(s):  
Fault Diagnosis ◽  
Support Vector Machines ◽  
High Altitude ◽  
Practical Significance ◽  
Support Vector ◽  
Diagnosis System ◽  
Intelligent Diagnosis ◽  
Intermediate Range ◽  
Vector Machines

Surface-to-air missile equipment is an advanced aerial defense weapon equipment of middle-high altitude intermediate range in our army, and this weapon equipment is also shouldering the significant task of antiaircraft defense of our country. Therefore, researching on its Fault Intelligent Diagnosis System has an important practical significance and military value on improving the weapon equipment’s renewing of fault and keeping the army’s battle effectiveness.

Studies and Application of Heavy Equipment Fault Diagnosis System

2011 ◽  
Vol 225-226 ◽  
pp. 399-402
Author(s):  
Yi Gan ◽  
Sha Liu ◽  
Wen Bo Zhu
Keyword(s):  
Fault Diagnosis ◽  
Fault Location ◽  
Diagnostic System ◽  
Diagnosis System ◽  
Initial Application ◽  
Heavy Equipment ◽  
Fault Diagnosis System ◽  
The Difference ◽  
Fault Information ◽  
Information Database

Analyze the ways to get fault information for heavy equipment fault diagnosis system, which are the control system of the device, layout sensors to get the key performance parameters, and human-computer interaction. In order to improve accuracy and efficiency of the diagnostic system, the methods of fault location tree retrieval and similar case retrieval are applied respectively according to the difference of fault information content in the diagnosis information database. The diagnosis system introduced in the paper gets effective initial application in the heavy equipment fault diagnosis system.

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