scholarly journals Three Phase Fault Diagnosis Based on RBF Neural Network Optimized By PSO Algorithm

2011 ◽  
Vol 5 (4) ◽  
pp. 181-185
Author(s):  
M. Sivakumar ◽  
R.M.S. Parvathi
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
Pso Algorithm ◽  
Three Phase

Fault Diagnosis of Induction Motors Based on RBF Neural Network

2013 ◽  
Vol 462-463 ◽  
pp. 85-88 ◽  
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Induction Motors ◽  
Rbf Neural Network ◽  
Short Circuit ◽  
Three Phase ◽  
Diagnosis Accuracy ◽  
Simulation Results

In order to improve the diagnosis accuracy of stator short circuit faults of three-phase induction motors, in this paper, a method using three-layered RBF neural network is proposed to diagnose the short circuit faults on the basis of analysis of structure and algorithm of RBF neural network. Then the approach to establish RBF neural network and the influence of different expanding coefficients upon the diagnosis accuracy are illustrated. The simulation results show that RBF neural network can successfully diagnose and classify six typical short circuit faults of induction motors. This method has a faster speed, higher accuracy and it needs fewer samples. In conclusion, RBF neural network is practical, efficient and intelligent in fault diagnosis of induction motors.

Information-Applied Technology in Fault Diagnosis Model of RBF Neural Network Based on PSO Algorithm for Analog Circuit

2014 ◽  
Vol 540 ◽  
pp. 452-455
Author(s):  
Xiao Hua Zhang ◽  
Hua Ping Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Analog Circuit ◽  
Rbf Neural Network ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Connection Weight ◽  
Diagnosis Method ◽  
The Individual ◽  
Network Diagnosis

To improve the ability of fault diagnosis for analog circuit, a RBF neural network diagnosis method trained by an improved Particle Swarm Optimization (PSO) algorithm is proposed. In order to overcome the shortcoming of the traditional BP algorithm of RBF neural network, PSO algorithm is introduced to optimize the center, width and connection weight of RBF neural network. And the mutation operator is inserted to ensure the individual in swarm out of the local optimum. The simulation shows that the proposed modeling algorithm has the better convergence and diagnosis characteristics.

Research on Intelligent Diagnosis Technology of Transformer Fault

2013 ◽  
Vol 385-386 ◽  
pp. 589-592
Author(s):  
Hong Qi Wu ◽  
Xiao Bin Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Intelligent Diagnosis ◽  
Rbf Network ◽  
Working Principle ◽  
Research On Application ◽  
Transformer Fault

In order to improve the diagnosis rates of transformer fault, a research on application of RBF neural network is carried out. The structure and working principle of radial basis function (RBF) neural network are analyzed and a three layer RBF network is also designed for transformer fault diagnosis. It is proved by MATLAB experiment that RBF neural network is a strong classifier which is used to diagnose transformer fault effectively.

A Fault Diagnosis Approach for Rolling Element Bearing Based on S-Transform and Artificial Neural Network

2017 ◽  
Author(s):  
Ningbo Zhao ◽  
Hongtao Zheng ◽  
Lei Yang ◽  
Zhitao Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Health Status ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
Rolling Element Bearing ◽  
S Transform ◽  
Rolling Element ◽  
Element Bearing ◽  
Diagnosis Approach

The condition monitoring and fault diagnosis of rolling element bearing is a very important research content in the field of gas turbine health management. In this paper, a hybrid fault diagnosis approach combining S-transform with artificial neural network (ANN) is developed to achieve the accurate feature extraction and effective fault diagnosis of rolling element bearing health status. Considering the nonlinear and non-stationary vibration characteristics of rolling element bearing under stable loading and rotational speeds, S-transform and singular value decomposition (SVD) theory are firstly used to process the vibration signal and extract its time-frequency information features. Then, radical basis function (RBF) neural network classification model is designed to carry out the state pattern recognition and fault diagnosis. As a practical application, the experimental data of rolling element bearing including four health status are analyzed to evaluate the performance of the proposed approach. The results demonstrate that the present hybrid fault diagnosis approach is very effective to extract the fault features and diagnose the fault pattern of rolling element bearing under different rotor speed, which may be a potential technology to enhance the condition monitoring of rotating equipment. Besides, the advantages of the developed approach are also confirmed by the comparisons with the other two approaches, i.e. the Wigner-Ville (WV) distribution and RBF neural network based method as well as the S-transform and Elman neural network based one.

Fault Diagnosis Analysis of Rotor System Based on RBF Neural Network and Dynamic Systems

2012 ◽  
Vol 460 ◽  
pp. 127-130
Author(s):  
Song He Zhang ◽  
Yue Gang Luo ◽  
Bin Wu ◽  
Bing Cheng Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
Amplitude Spectrum ◽  
Rates Of Convergence ◽  
Rotor System ◽  
Rbf Network ◽  
Characteristic Quantity ◽  
Learning Rates ◽  
Fault Characteristic

The RBF network was applied in the rotor system to realize the fault diagnosis aiming the mapping complexity between fault symptoms and fault patterns. It can overcome the problems of low learning rates of convergence and falling easily into part minimums in BP algorithm, and improve the precision of diagnosis. The normalized values of seven frequency ranges in amplitude spectrum were used as the fault characteristic quantity, the RBF network was trained to diagnose the faults of rotor system. The results show that RBF neural network is a valid method of diagnosis of mechanical failure.

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