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.

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.

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.

scholarly journals Equipment Maintenance Support Effectiveness Evaluation Based on Improved Generative Adversarial Network and Radial Basis Function Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhen Li ◽  
Jianping Hao ◽  
Cuijuan Gao
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Effectiveness Evaluation ◽  
Generative Adversarial Network ◽  
Generalization Ability ◽  
Rbf Network ◽  
Adversarial Network ◽  
Radial Basis ◽  
Network Evaluation

Due to the lack of maintenance support samples, maintenance support effectiveness evaluation based on the deep neural network often faces the problem of small sample overfitting and low generalization ability. In this paper, a neural network evaluation model based on an improved generative adversarial network (GAN) and radial basis function (RBF) network is proposed to amplify maintenance support samples. It adds category constraint based on category probability vector reordering function to GAN loss function, avoids the simplification of generated sample categories, and enhances the quality of generated samples. It also designs a parameter initialization method based on parameter components equidistant variation for RBF network, which enhances the response of correct feature information and reduces the risk of training overfitting. The comparison results show that the mean square error (MSE) of the improved GAN-RBF model is 5.921 × 10 − 4 , which is approximately 1/2 of the RBF model, 1/3 of the Elman model, and 1/5 of the BP model, while its complexity remains at a reasonable level. Compared with traditional neural network evaluation methods, the improved GAN-RBF model has higher evaluation accuracy, better solves the problem of poor generalization ability caused by insufficient training samples, and can be more effectively applied to maintenance support effectiveness evaluation. At the same time, it also provides a good reference for evaluation research in other fields.

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.

Radial Basis Function Networks for Analog Circuit Fault Isolation

1997 ◽  
Vol 07 (06) ◽  
pp. 643-655 ◽  
Author(s):  
N. S. C. Babu ◽  
V. C. Prasad
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Analog Circuits ◽  
Basis Function ◽  
Analog Circuit ◽  
Selection Procedure ◽  
Fault Isolation ◽  
Fault Dictionary ◽  
Rbf Network ◽  
Radial Basis

The application of a radial basis function neural network (RBFN) for analog circuit fault isolation is presented. In this method the RBFN replaces the fault dictionary of analog circuits. The proposed method for analog circuit fault isolation takes the advantage of extremely fast training of RBFN compared to earlier neural network methods. A method is suggested to select centers and widths of RBF units. This selection procedure accounts for the component tolerances. The effectiveness of the RBFN for the fault isolation problem is demonstrated with an illustrative example. RBFN performed well even when the input patterns are drawn directly from the test node voltages of the analog circuit under consideration. A method is suggested to modify the RBF network in the event of occurrence of a new fault. The suggested modifications do not affect the previous training.

Sign in / Sign up

Export Citation Format

Share Document