Study on B/S-Based Remote Fault Diagnosis System of Radar Equipment

2011 ◽  
Vol 383-390 ◽  
pp. 5605-5608
Author(s):  
Zhi Jian Sun ◽  
Xian Zhi Zhang ◽  
Wen Sheng ◽  
Shi Hua Liu
Keyword(s):  
Fault Diagnosis ◽  
Web Based ◽  
Diagnosis System ◽  
Key Technology ◽  
System A ◽  
Radar Network ◽  
Key Points ◽  
Fault Diagnosis System ◽  
Function Module ◽  
Set Up

This paper discusses a Web-based remote radar fault diagnosis and information system . A delicated radar network is set up with knowledge base and Web . The various key points about the structure , the function module and the key technology of the remote diagnosis system are analyzed and developed . In this way the real-time monition and automatical fault and diagnosis are realized , the reliability of the radar and the emergency repairing ability is improved .

Remote Fault Diagnosis System of Ultrahigh Speed Grinding Based on Multi-Agent

2009 ◽  
Vol 76-78 ◽  
pp. 67-71
Author(s):  
Wan Shan Wang ◽  
Tian Biao Yu
Keyword(s):  
Fault Diagnosis ◽  
Theoretical Research ◽  
Case Based Reasoning ◽  
Diagnosis System ◽  
Knowledge Reasoning ◽  
Diagnosis Method ◽  
Multi Agent ◽  
Fault Diagnosis System ◽  
Set Up ◽  
Remote Fault Diagnosis

A remote fault diagnosis method for ultrahigh speeding grinding based on multi-agent is presented. The general faults of ultrahigh speed grinding are analyzed and diagnosis model based on multi-agent is established, the dialogue layer, problem decomposition layer, control layer and problem solving layer in the process of diagnosis are studied and the knowledge reasoning model of fault diagnosis is set up based case-based reasoning (CBR) combining rule-based reasoning (RBR). Based on theoretical research, a remote fault diagnosis system of ultrahigh speed grinding is developed. Results of the system running prove the theory is correctness and the technology is feasibility.

Modeling a web-based remote monitoring and fault diagnosis system with UML and component technology

2006 ◽  
Vol 27 (1) ◽  
pp. 5-19 ◽  
Author(s):  
Xing Wu ◽  
Jin Chen ◽  
Ruqiang Li ◽  
Weixiang Sun ◽  
Guicai Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Remote Monitoring ◽  
Component Technology ◽  
Web Based ◽  
Diagnosis System ◽  
Fault Diagnosis System

The Development of Embedded Rotating Machine Fault Diagnosis System Based on Qt/Embedded

2011 ◽  
Vol 328-330 ◽  
pp. 933-938
Author(s):  
Ze Min Zhou ◽  
Chun Liang Zhang ◽  
Yue Hua Xiong
Keyword(s):  
Fault Diagnosis ◽  
Data Storage ◽  
Diagnostic Model ◽  
Diagnosis System ◽  
Rotating Machine ◽  
Almost Everywhere ◽  
Fault Diagnosis System ◽  
Model Training ◽  
And Performance ◽  
Set Up

As the constantly growth of embedded Technology, embedded products put into use in almost everywhere around our life. This paper mainly introduce how to set up the Qt/Embedded Cross-compiling environment in Linux system platform, and the development and transplant of embedded fault diagnosis system use development tools of Qt/Embedded. This system achieve those functions, including fault display, signal analysis, diagnostic model training and data storage or transmittal, by graphical interfaces designing and performance function addition. And finally create the application document which applies to ARM framework embedded processor by Cross-compiling. In the end, the application document is transplanted and run in the target platform, and testing the diagnosis effects.

Artificial Neural Network in the Autopilot System Application of Troubleshooting

2012 ◽  
Vol 263-266 ◽  
pp. 3198-3202
Author(s):  
Peng Zhang ◽  
Shi Chao Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Fault Isolation ◽  
Bp Algorithm ◽  
Diagnosis System ◽  
System A ◽  
Neural Network Structure ◽  
Fault Diagnosis System ◽  
Artificial Neural

the existing fault diagnosis system in fault detection aspects of Boeing 737 A/P is effective, but in fault isolation aspects performance is poor, therefore using ANN technology need to improve its diagnosis system. A/P for the typical fault, the three layers feed forward artificial neural network structure, this paper introduces the conjugate gradient BP algorithm and gives the diagnosis results. Diagnosis results show that artificial neural network can accurately identify system three typical faults, improve the efficiency of fault diagnosis and fault isolation capability.

Pipeline Fault Diagnosis System Enhancement

1998 ◽  
Author(s):  
Tang Xiujia
Keyword(s):  
Fault Diagnosis ◽  
Wave Form ◽  
Negative Wave ◽  
Diagnosis System ◽  
Temperature Characteristics ◽  
Syntactic Pattern ◽  
Higher Temperature ◽  
Fault Diagnosis System ◽  
Set Up ◽  
Data Context

In order to decrease the false alarm rate and improve the sensitivity of pipeline fault diagnosis system, three artificial intelligence based methods are first proposed. Neural networks with the input matrix composed by stress wave characteristics in time domain or frequency domain is proposed to classify various situations of the pipeline, in order to detect the leakage from pipeline online running data. Context-free grammar of symbolic representation of the negative wave form is used and a negative wave form parsing system with application to syntactic pattern recognition based on the representation is described. New complex thermal and hydraulic models, in which the flow regime, viscosity-temperature characteristics, density-temperature characteristics and specific heat-temperature characteristics, etc., of the running fluid in the pipelines are set up for non-isothermal pipeline carrying higher temperature fluid or in ambient environment.

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