Research on Fault Diagnosis Model of Tank Equipment

2013 ◽  
Vol 329 ◽  
pp. 359-363
Author(s):  
Zeng Zhang ◽  
Ming Yang ◽  
Bao Liang Dong ◽  
Xiao Bo Wang
Keyword(s):  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Network Theory ◽  
Short Wave ◽  
Performance Model ◽  
Intelligent Fault Diagnosis ◽  
Wave Radio ◽  
Diagnosis Method ◽  
The Common ◽  
Neural Network Theory

Taking the common fault to a certain type of short-wave radio as an example, Bayesian network, expert system and BP neural network theory were used to construct three intelligent fault diagnosis models, respectively by Genie, CLIPS and MATLAB software simulation. The results show that the three intelligent fault diagnosis method compared with the traditional method, a rapid and accurate diagnostic performance model can be extended to other communications and electronic equipment fault diagnosis.

Fault Diagnosis for Electrical Control System of Automobile Based on Support Vector Machine

2014 ◽  
Vol 666 ◽  
pp. 203-207
Author(s):  
Jian Hua Cao
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Control System ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Experimental Results ◽  
Support Vector ◽  
Electrical Control ◽  
Diagnosis Method ◽  
The Common

This paper is to present a fault diagnosis method for electrical control system of automobile based on support vector machine. We collect the common fault states of electrical control system of automobile to analyze the fault diagnosis ability of electrical control system of automobile based on support vector machine. It can be seen that the accuracy of fault diagnosis for electrical control system of automobile by support vector machine is 92.31%; and the accuracy of fault diagnosis for electrical control system of automobile by BP neural network is 80.77%. The experimental results show that the accuracy of fault diagnosis for electrical control system of automobile of support vector machine is higher than that of BP neural network.

Rolling Bearing Fault Diagnosis Based on Wavelet Packet- Neural Network Characteristic Entropy

2010 ◽  
Vol 108-111 ◽  
pp. 1075-1079 ◽  
Author(s):  
Li Ying Wang ◽  
Wei Guo Zhao ◽  
Ying Liu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Wavelet Packet Decomposition ◽  
Intelligent Fault Diagnosis ◽  
Network Characteristic ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

On the basis of neural network based on wavelet packet-characteristic entropy(WP-CE) the author proposes a new fault diagnosis method of vibrating of hearings, in which three layers wavelet packet decomposition of the acquired vibrating signals of hearings is performed and the wavelet packet-characteristic entropy is extracted, the eigenvector of wavelet packet of the vibrating signals is constructed，and taking this eigenvector as fault sample the three layers BP neural network is trained to implement the intelligent fault diagnosis. The simulation result from the proposed method is effective and feasible.

Research on Roller Bearing with Fault Diagnosis Method Based on EMD and BP Neural Network

2014 ◽  
Vol 1014 ◽  
pp. 501-504 ◽  
Author(s):  
Shu Guo ◽  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Kun Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Classification Performance ◽  
Outer Race ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Main Fault

In order to discover the fault with roller bearing in time, a new fault diagnosis method based on Empirical mode decomposition (EMD) and BP neural network is put forward in the paper. First, we get the fault signal through experiments. Then we use EMD to decompose the vibration signal into a series of single signals. We can extract main fault information from the single signals. The kurtosis coefficient of the single signals forms a feature vector which is used as the input data of the BP neural network. The trained BP neural network can be used for fault identification. Through analyzing, BP neural network can distinguish the fault into normal state, inner race fault, outer race fault. The results show that this method can gain very stable classification performance and good computational efficiency.

Fault diagnosis method for scintillation detector based on BP neural network

2021 ◽  
Vol 16 (07) ◽  
pp. T07006
Author(s):  
Y.X. Xie ◽  
Y.J. Yan ◽  
X. Li ◽  
T.S. Ding ◽  
C. Ma
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Scintillation Detector ◽  
Diagnosis Method
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