Study of Camshaft Grinders Faults Prediction Based on RBF Neural Network

2011 ◽  
Vol 141 ◽  
pp. 519-523
Author(s):  
Ting Dong ◽  
Hong Jun Wang ◽  
Lei Shi
Keyword(s):  
Neural Network ◽  
Production Line ◽  
Manufacturing Systems ◽  
Production Efficiency ◽  
Clustering Algorithm ◽  
Rbf Neural Network ◽  
Approximation Error ◽  
Maintenance Schedule ◽  
Time Between Failures ◽  
Production Type

Maintenance schemes in manufacturing systems are devised to reset the machines functionality in an economical fashion and keep it within acceptable levels. Camshaft grinders play the important role for the camshaft production line which is the massive production type. The camshaft grinders working condition is one of the critical sections which affected the production efficiency and profit of the manufactures. Nowadays the maintenance based on condition is carried out in order to meet the requirements of the market. The Time Between Failures (TBF) could be used for arranging the maintenance schedule. The faults prediction model based on RBF neural network, adopted K-means clustering algorithm to select clustering centre of radial basis function neural network (RBFNN), is proposed for the camshaft grinders which are the key equipment of camshaft production line. The TBF of the camshaft grinders are predicted by using this model, where the distribution density is 1, with the accepted network approximation error. An industrial example is used to illustrate the application of this model. The proposed method is effective and can be used for the suggestions for the practical workshop machines maintenance.

scholarly journals Adaptive PD Control Based on RBF Neural Network for a Wire-Driven Parallel Robot and Prototype Experiments

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Yuqi Wang ◽  
Qi Lin ◽  
Xiaoguang Wang ◽  
Fangui Zhou
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control Method ◽  
Rbf Neural Network ◽  
Approximation Error ◽  
Wind Tunnel Test ◽  
Parallel Robot ◽  
Network Control ◽  
Pd Control ◽  
Control Scheme

An adaptive PD control scheme is proposed for the support system of a wire-driven parallel robot (WDPR) used in a wind tunnel test. The control scheme combines a PD control and an adaptive control based on a radial basis function (RBF) neural network. The PD control is used to track the trajectory of the end effector of the WDPR. The experimental environment, the external disturbances, and other factors result in uncertainties of some parameters for the WDPR; therefore, the RBF neural network control method is used to approximate the parameters. An adaptive control algorithm is developed to reduce the approximation error and improve the robustness and control precision of the WDPR. It is demonstrated that the closed-loop system is stable based on the Lyapunov stability theory. The simulation results show that the proposed control scheme results in a good performance of the WDPR. The experimental results of the prototype experiments show that the WDPR operates on the desired trajectory; the proposed control method is correct and effective, and the experimental error is small and meets the requirements.

The Poultry Meat Production Technical Efficiency Analysis Model Based on Intelligent Devices

2015 ◽  
Vol 713-715 ◽  
pp. 2181-2184
Author(s):  
Xiang Jie Niu ◽  
Hua Li
Keyword(s):  
Neural Network ◽  
Production Efficiency ◽  
Rbf Neural Network ◽  
Efficiency Analysis ◽  
Poultry Meat ◽  
Meat Production ◽  
Analysis Method ◽  
Analysis Model ◽  
Neural Network Algorithm ◽  
Intelligent Devices

The paper focuses on the poultry meat production efficiency analysis methods. In the poultry meat production procedure, the randomness is strong due to the various complex factors in the poultry meat production which will reduce the production efficiency. In order to avoid the defects of the traditional algorithms, the paper proposes the poultry meat production efficiency analysis method based on RBF neural network algorithm. The effect factors in the poultry meat production will be sifted as the basic data for production efficiency analysis. The RBF neural network model is built and the output results are used to analyze the poultry meat production efficiency with intelligent devices. The experiment results illustrate the improved algorithm can increase the poultry meat production efficiency.

Design of RBF Neural Networks Based on Adjustable Radius

2010 ◽  
Vol 439-440 ◽  
pp. 605-610
Author(s):  
Xiao Yong Liu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Function Approximation ◽  
Clustering Algorithm ◽  
Rbf Neural Network ◽  
Gaussian Function ◽  
Mean Square ◽  
Rbf Neural Networks ◽  
Data Points ◽  
Better Than

In this paper, a new RBF neural network (RBFNN) algorithm, called ar-RBFNN, is presented. In traditional RBFNNs based on clustering algorithm, called oRBFNN in this paper, the width of the basis function-Gaussian function, or called radius, ignored the effect of numbers in different clusters, or density of data points. New algorithm considers radius is effect to performance of algorithms in problem of function approximation. Mean Square Error is used to evaluate performances of two algorithms, oRBFNN and ar-RBFNN algorithms. Several experiments in function approximation show ar-RBFNN is better than oRBFNN.

Quantum-Behaved Particle Swarm Optimization Based Radial Basis Function Network for Classification of Clinical Datasets

2018 ◽  
Vol 9 (2) ◽  
pp. 32-52
Author(s):  
N. Leema ◽  
H. Khanna Nehemiah ◽  
A. Kannan
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Radial Basis Function ◽  
Basis Function ◽  
Clustering Algorithm ◽  
Radial Basis Function Network ◽  
Approximation Error ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Radial Basis

In this article, a classification framework that uses quantum-behaved particle swarm optimization neural network (QPSONN) classifiers for diagnosing a disease is discussed. The neural network used for classification is radial basis function neural network (RBFNN). For training the RBFNN K-means clustering algorithm and quantum-behaved particle swarm optimization (QPSO) algorithm has been used. The K-means clustering algorithm is used to find the optimal number of clusters which determines the number of neurons in the hidden layer. The cluster approximation error is used to find the optimal clusters. The weights between the hidden and the output layer is determined using QPSO algorithm based on the mean squared error (MSE). The performance of the developed classifier model has been tested with five clinical datasets, namely Pima Indian Diabetes, Hepatitis, Bupa Liver Disease, Wisconsin Breast Cancer and Cleveland Heart Disease were obtained from the University of California, Irvine (UCI) machine learning repository.

Kinematics Research of 6R Arc Welding Robot Based on Radial Basis Function Neural Network

2012 ◽  
Vol 163 ◽  
pp. 247-250
Author(s):  
De Zheng Song ◽  
Chao Yun
Keyword(s):  
Neural Network ◽  
Arc Welding ◽  
Inverse Kinematics ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Rbf Neural Network ◽  
Simple Calculation ◽  
Weight Coefficient ◽  
Welding Robot ◽  
Six Dof

Take serial robot with six DOF for example. On the basis of analyzing the characteristics of RBF neural network, inverse kinematics calculation of arc welding robot was achieved by RBF of six-input and single output. The forward and inverse kinematics could be seen as a nonlinear mapping between the joint space and the operation space of the robot. Take the algorithm based on RBF. Acquire RBF centers by the nearest neighbor clustering algorithm. The inverse kinematics of robot was solved. Through learning the training samples of the positive solutions to determine weight coefficient of neural network, the robots pose could be accurately solved. The example shows that the algorithm has the characteristics of simple calculation and effective solution, etc. The cumbersome derivation of traditional methods is avoided. It can be seen as kinematics trajectory tracking controller of serial mechanism system.

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