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.

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.

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.

Application of RBF Neural Network in Fault Diagnosis for Transmission Gear

2012 ◽  
Vol 433-440 ◽  
pp. 7563-7568 ◽  
Author(s):  
Xi Mei Liu ◽  
Xiao Hui Yao ◽  
Qian Zhao ◽  
Hong Mi Guo
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Rbf Neural Network ◽  
Broad Application ◽  
Rbf Network ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
Neural Network Toolbox ◽  
Transmission Gear

A method for transmission gearbox fault diagnosis is put forward in this paper by using radial basis function neural network (RBF network). A RBF neural network is created to simulate the gearbox fault diagnosis using Matlab neural network toolbox. Compared with BP neural network, RBF network is superior to the former in accuracy and speed according to the simulate results. This method is accurate and credible in gear fault diagnosis, and it has a broad application prospect in mechanical fault diagnosis.

Garbage Crusher Fault Diagnosis Based on RBF Neural Network

2009 ◽  
Vol 16-19 ◽  
pp. 971-975
Author(s):  
Yong Hou Sun ◽  
Cong Li ◽  
Mei Fa Huang ◽  
Hui Jing
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
Structure And Properties ◽  
Automatic Equipment ◽  
Rbf Network ◽  
The Neural Network ◽  
Diagnosis Model ◽  
Simulation Results ◽  
Training Simulation

The garbage crusher is a new kind of crusher for garbage crushing when processing Municipal Solid Waste (MSW). With the development of automatic equipment and the complication of structure and properties of the garbage crusher, the fault diagnosis of garbage crusher is very important. In this paper, according to the fault symptoms and parameters, Radial Basis Function Neural Network (RBF NN) is used for fault diagnosis of the garbage crusher. The structure and inference of RBF NN are discussed in detail. The garbage crusher fault diagnosis model is established based on RBF network. At last, the fault of mechanical system is taken as an example of garbage crusher fault diagnosis. Training simulation results of the neural network are given base on MATLAB software. The result shows the RBF NN is suitable for fault diagnosis of garbage crusher.

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.

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.

Prediction on China's Energy Demand Based on RBF Neural Network Model

2013 ◽  
Vol 805-806 ◽  
pp. 1421-1424
Author(s):  
Xue Feng ◽  
Wuyunbilige Bao ◽  
Ben Ha
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Neural Network Model ◽  
Energy Demand ◽  
Rbf Neural Network ◽  
Bp Network ◽  
Factors Affecting ◽  
Rbf Network ◽  
Network Prediction ◽  
Main Factors

Choose factors which influence the energy demand by the method of path analysis, build radial basis function (RBF) neural network model to predict energy demand in China. The RBF neural network is trained with the actual data of the main factors affecting energy demand during 1989-2003 and energy demand during 1993-2007 as learning sample with a good fitting effect. After testing network with the actual data of the main factors affecting energy demand during 2004-2007 and energy demand during 2008-2011, higher prediction accuracy can be obtained. By comparison with the BP network, RBF network prediction model outperforms BP network prediction model, finally RBF network is applied to make prediction of energy consumption for the year 2013-2015.

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