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.

Roller Element Bearing of Mine Ventilating Fan with Fault Diagnosis Based on Mechanics Properties and RBF Neural Network

2012 ◽  
Vol 625 ◽  
pp. 125-129
Author(s):  
Ying Hong Zhang ◽  
Cong Li ◽  
Hui Jing ◽  
Bing Bing Gao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Rbf Network ◽  
Bearing Fault Diagnosis ◽  
Element Bearing ◽  
Diagnosis Model

Roller element bearing is an important part of mine ventilating fan. The management and maintenance of the equipment is very important. Therefore, it is necessary to employ fault diagnosis process to the roller element bearing. In this paper, mechanics properties of roller element bearing are analyzed. Then, Radial Basis Function (RBF) neural network is used for the fault diagnosis of the roller element bearing. The structure and inference of RBF network are discussed in detail. The roller element bearing fault diagnosis model is established based on RBF network. A case study is given. The proposed method is applied to the fault diagnosis of roller element bearing. The result shows that the proposed method can improve efficiency of the fault diagnosis.

scholarly journals Crack Fault Diagnosis and Location Method for a Dual-disks Hollow Shaft Rotor System Based on the Radial Basis Function Network and Pattern Recognition Neural Network

2020 ◽  
Author(s):  
Yuhong Jin ◽  
Lei Hou ◽  
Zhenyong Lu ◽  
Yushu Chen
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Fault Diagnosis ◽  
Dynamic Response ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rotor System ◽  
Rbf Network ◽  
Hollow Shaft ◽  
Whirl Speed

Abstract In recent years, the crack fault is one of the most common faults in the rotor system, and its fault diagnosis has been paid close attention by researchers. However, the traditional fault diagnosis methods based on various signal processing algorithms can only be adopted to determine whether there is a crack fault in the rotor system, but the dynamic response of the rotor system can hardly be used to calculate the depth and position of the crack. In this paper, a new crack fault diagnosis and location method for a dual-disks hollow shaft rotor system based on the Radial basis function (RBF) network and Pattern recognition neural network (PRNN) is presented. Firstly, a rotor system model with a breathing crack suitable for a short-thick hollow shaft rotor is established based on the finite element method and Timoshenko beam theory. Then the dynamic response is calculated by the harmonic balance method and the analysis results show that the first critical whirl speed, the first subcritical speed, the first critical speed amplitude, and the super-harmonic resonance peak at 1/2 first critical whirl speed of the rotor system are closely related to the depth and position of the crack, which can be used for crack fault diagnosis. Finally, the RBF network and PRNN are adopted to determine the depth and approximate location of the crack by taking the above dynamic response characteristics as input, respectively. The test results show that this method has high fault diagnosis accuracy.

Shunt Active Filter Based on Radial Basis Function Neural Network and p-q Power Theory

2017 ◽  
Vol 8 (2) ◽  
pp. 667 ◽  
Author(s):  
Prakash Ch. Tah ◽  
Anup K. Panda ◽  
Bibhu P. Panigrahi
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Control Strategies ◽  
New Combination ◽  
Training Methods ◽  
Power Theory ◽  
Rbf Network ◽  
Radial Basis

In this paper a new combination Radial Basis Function Neural Network and p-q Power Theory (RBFNN-PQ) proposed to control shunt active power filters (SAPF). The recommended system has better specifications in comparison with other control methods. In the proposed combination an RBF neural network is employed to extract compensation reference current when supply voltages are distorted and/or unbalance sinusoidal. In order to make the employed model much simpler and tighter an adaptive algorithm for RBF network is proposed. The proposed RBFNN filtering algorithm is based on efficient  training methods called hybrid learning method.The method  requires a small size network, very robust, and the proposed algorithms are very effective. Extensive simulations are carried out with PI as well as RBFNN controller for p-q control strategies by considering different voltage conditions and adequate results were presented.

The Study of Precipitation Forecast Model on EMD-RBF Neural Network - A Case Study on Northeast China

2014 ◽  
Vol 641-642 ◽  
pp. 119-122 ◽  
Author(s):  
Xiao Sun ◽  
Shi Fan Qiao ◽  
Ji Ren Xie
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Precipitation Forecast ◽  
Maximum Relative Error ◽  
Precipitation Data ◽  
Rbf Network ◽  
Precipitation Forecasting ◽  
Radial Basis

Based on the principal of forecast of Artificial Neural Network, Radial Basis Function neural network and Radial Basis Function neural network based on EMD were introduced into the field of precipitation forecasting in this article. With the precipitation data of 27 sites from1950-2010, EMD-RBF network was set up, and the difference between the predictive value and the actual precipitation data was discussed. The results showed that the correlation Of EMD-RBF forecast precipitation and actual precipitation is more than 0.9. Of all sites, the maximum relative prediction error of 17 sites is less than 10%, the maximum relative error does not exceed 15%.The EMD-RBF model had good quality on forecasting precision, which provided a new method for precipitation forecasting.

scholarly journals Rainfall Runoff Modeling using Radial Basis Function Neural Network for Sungai Tinjar Catchment, Miri, Sarawak

2009 ◽  
Vol 1 (1) ◽  
pp. 1-7
Author(s):  
Suhaimi S. ◽  
Rosmina A. Bustami
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Rainfall Runoff ◽  
Optimum Result ◽  
Rbf Network ◽  
Linear Relationships ◽  
Radial Basis ◽  
Artificial Neural Network Ann

Artificial Neural Network (ANN) is a very useful data modelling tool that is able to capture and represent complex input and output relationships. The advantage of ANN lies in its ability to represent both linear and non-linear relationships and in its ability to learn these relationships directly from the data being modelled. Modeling of rainfall runoff relationship is important in view of the many uses of water resources such as hydropower generation, irrigation, water supply and flood control.This study is to purposefully develop a rainfall runoff model for Sg. Tinjar with outlet at Long Jegan using Radial Basis Function (RBF) Neural Network. Training and simulation was done using Matlab 6.5.1 software with varying parameters to obtain the optimum result. Further, the results were compared to simulation done with Multilayer Percepteron model. The RBF network developed in this study has successfully modelled rainfall runoff relationship in Sungai Tinjar Catchment in Miri, Sarawak with an accuracy of about 98.3%.

Direct Adaptive Control for a Certain Class of Nonlinear Systems Using Modified Radial Basis Function Neural Network

Volume 1 ◽  
2004 ◽  
Author(s):  
Hsuan-Ju Chen ◽  
Rongshun Chen
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Radial Basis Function ◽  
Basis Function ◽  
Controller Design ◽  
Rbf Neural Network ◽  
Design Parameters ◽  
State Variables ◽  
Rbf Network ◽  
Radial Basis

This paper proposes a direct adaptive controller for SISO affine nonlinear systems using Gaussian radial basis function (RBF) neural network (NN). The exact plant model is not necessary for composing the controller. If the plant is SISO, of affine form, without zero dynamics, and all the state variables are available, the controller is applicable under several mild assumptions. In this paper, the Gaussian RBF network (GRBFN) is modified to include pre-scale weights as its parameters for the input variables, which are also adapted in the control law. Pre-scaling the inputs is equivalent to extending or contracting the spectrum of the approximated function. With the modification, the spectrum along each coordinate of the domain can be scaled separately for approximating. The adaptation of the nonlinear parameters, including the variances, centers, and pre-scaling weights, are derived. Appropriate modification techniques are applied to the adaptation laws to ensure the robustness. The stability is analyzed with Lyapunov’s Theory. From the analysis, the effect of the controller design parameters is also examined. A simulation of an inverted pendulum control is demonstrated to show the effectiveness.

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