Application of Integrated Neural Network Information Fusion in Fault Diagnosis of one Missile Launching Control Unit

2013 ◽  
Vol 427-429 ◽  
pp. 2808-2812
Author(s):  
Xu De Cheng ◽  
Hong Li Wang ◽  
Bing Xu ◽  
Xue Dong Xue
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Preliminary Analysis ◽  
Control Unit ◽  
Fusion Technology ◽  
Network Information ◽  
Diagnosis Rate ◽  
Fault Diagnosis System ◽  
Made In

Research and development of fault diagnosis system in application of integrated neural network information fusion is based on information fusion technology, with which preliminary analysis of equipment fault is made in different perspectives in terms of neural network, so as to identify the fault on the basis of fusion outcome. This technique is applied in fault diagnosis of one type of missile launching control unit, leading to sufficient use of various information and substantially increased fault diagnosis rate.

Based on Information Fusion Integrated Wavelet Neural Network Fault Diagnosis

2011 ◽  
Vol 219-220 ◽  
pp. 1077-1080
Author(s):  
Dong Yan Cui ◽  
Zai Xing Xie
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Wavelet Neural Network ◽  
Wavelet Neural Networks ◽  
Effective Combination ◽  
Fault Diagnosis System ◽  
Network Fault ◽  
Fault Characteristic

In this paper, the integration of wavelet neural network fault diagnosis system is established based on information fusion technology. the effective combination of fault characteristic information proves that integration of wavelet neural networks make better use of a variety of characteristic information than the list of wavelet neural networks to solve difficulties and problems which are difficult to resolve by a single network.

The Motor Fault Diagnosis Based on Neural Network and the Theory of D-S Evidence

2013 ◽  
Vol 683 ◽  
pp. 881-884 ◽  
Author(s):  
Chang Fei Sun ◽  
Zhi Shan Duan ◽  
Yang Yang ◽  
Miao Wang ◽  
Li Jie Hu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Threshold Value ◽  
Fusion Technology ◽  
Independent Evidence ◽  
Modes Of Failure ◽  
Processing Data ◽  
Single Feature ◽  
Diagnosis Method

In order to reduce the uncertainty of the traditional method that use a single parameter in the motor fault diagnosis, create a reliable motor fault diagnosis model by using multivariate information fusion technology and the combination of neural network and the theory of D-S evidence .First, fusion the information of many kinds of sensors, preliminary identify the modes of failure, find the information of different fault feature by analysing and processing data, establish the domain of feature. Then part diagnose the domain of feature by using neural network. The local diagnosis results form independent evidence body. Calculate the credibility of the fault distribution of each evidence body for recognition framework. It is difficult to discriminant fault types by directly using these reliability distribution. So choose appropriate D-S evidence formula to fusion each evidence body and further process and analyse the information of evidence. The credibility of the distribution is nearer and nearer to the judgement threshold value of fault types with one fusion, and the rest of the credibility distribution of the fault is more and more smaller. So the basic reliability distribution has better peak and separability, the diagnose results is more accurate, and finally achieve accurate diagnosis of the motor fault. The diagnosis example shows that the diagnosis method based on neural network and the theory of D-S evidence can realize comprehensive diagnosis of motor fault by using multi-resources information. The reliability and accuracy of this diagnosis method are far higher than that of the local diagnosis using single feature. It improves the precision of the motor fault diagnosis.

Fault Diagnosis Method of Time Domain and Time-Frequency Domain Based on Information Fusion

2013 ◽  
Vol 300-301 ◽  
pp. 635-639 ◽  
Author(s):  
Jiang Zhao ◽  
Jiao Wang ◽  
Meng Shang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Time Domain ◽  
Wavelet Packet ◽  
Network Information ◽  
Time Frequency ◽  
Index Data ◽  
Diagnosis Method ◽  
Frequency Band Energy

On account of the problem that traditional pipe leakage diagnosis method is not highly accuracy .this paper come up with a method that based on pipe leakage diagnosis method of neural network information fusion. Giving the stress wave time domain feature extraction index data algorithm and wavelet packet extraction each the frequency band energy algorithm, by comparing with these results of the pressure wave time domain feature index data, time-frequency extraction energy values and fault diagnosis of both information fusion ,which show the neural network information fusion method that is used for pipe leakage diagnosis that is feasible and effective.

Research on the Fault Diagnosis System for ICM

2013 ◽  
Vol 712-715 ◽  
pp. 2055-2058
Author(s):  
Zhong Nian Li ◽  
Lei Zhou
Keyword(s):  
Neural Network ◽  
Factor Analysis ◽  
Fault Diagnosis ◽  
Processing Speed ◽  
Fault Tolerant ◽  
Principal Factor ◽  
Diagnosis System ◽  
Principal Factor Analysis ◽  
Diagnosis Rate ◽  
Fault Diagnosis System

In the paper, researched a fault diagnosis system which is used in the ICM (Intelligent Coiling Machine)successfully, it is a kind of fault diagnosis system that uses compactwavelet neural network wavelet neural network as the intelligent core and has the PFA(Principal Factor Analysis) pretreatment function. Through innovative designing and carefully plotted monitoring ways and methods in the system, as a result the fault diagnosis rate of accuracy is high, fault-tolerant ability is strong, the processing speed is quick, and the system work safely and reliably.It has achieved the anticipated goal and the effect.

Sign in / Sign up

Export Citation Format

Share Document