计算机技术与发展2011，Vol.21Issue(1)：210-213,217,5.
基于L-M神经网络的齿轮故障诊断
Gear Fault Diagnosis Based on Levenberg-Marquardt Neural Network
摘要
Abstract
Because of the complexity of gear working condition, there are non-linear relationship between characteristic parameters and fault types. Proposes to apply the feed forward artificial neural network with Levenberg-Marquardt training algorithm, to the problem of gear fault diagnosis. By using second derivative information ,the network convergence speed is promoted and the generalization performance is enhanced. Taking a certain gearbox fault signal acquisition experimental system for an example, MATLAB software and its neural network toolbox are used to model and simulate. The experiment result shows that Levenberg-Marquardi neural network has good performance for the common gear fault diagnosis and it can identify various types of faults stably and accurately. Furthermore, compared with conventional BP neural network,the Levenberg-Marquardt neural network reduces training epochs and promotes prediction accuracy.关键词
神经网络/麦夸特算法/齿轮故障诊断分类
信息技术与安全科学引用本文复制引用
毛明明,柳益君,汤嘉立..基于L-M神经网络的齿轮故障诊断[J].计算机技术与发展,2011,21(1):210-213,217,5.基金项目
江苏省自然科学基础研究基金(07KJD20040) （07KJD20040）
