机械科学与技术2017，Vol.36Issue(6)：869-876,8.DOI:10.13433/j.cnki.1003-8728.2017.0608
采用神经网络的断路器传动机构磨损预测
Wear Prediction of Circuit Breaker Transmission Mechanism based on Neural Network
摘要
Abstract
Considering the feature that the wear experiment cost for the circuit breaker mechanism system is very high,the wear prediction model is established for the dangerous joint.Based on the pin-on-disc experiments data,two kinds of typical models are established for the wear prediction.The comparative study shows that the model based on the Elman neural network can accurately reflect the inherent wear law between wear rate and contact pressure,sliding velocity and material hardness with a high prediction precision.Considering the clearance in revolute joint,the kinematic parameters are obtained by using ADAMS software based on the elastic-damping contact force model.Then,the trained Elman model is employed to predict the wear of dangerous joint,using the parameters transformed by Hertz contact model.It can be seen from iterative analysis that wear occurs seriously in some special areas on bushing surface.The comparison result indicates that the wear calculation from prediction model is more useful for wear failure criteria than the Archard model which uses constant coefficient.关键词
断路器机构/磨损预测/神经网络/Archard模型Key words
circuit breaker mechanism/wear prediction/neural network/archard model分类
机械制造引用本文复制引用
刘创,刘宏昭..采用神经网络的断路器传动机构磨损预测[J].机械科学与技术,2017,36(6):869-876,8.基金项目
国家自然科学基金项目(51275404)资助 （51275404）
