Use of Neural Networks to Predict Rear Azle Gear Damage

摘要：

Accurate rear axle damage prediction is very difficult because of the rotating speeds and the changing loads when the truck is running.In this paper, a new method,which consists of a data pretrcatment (recursive processing) and artificial neural networks,is proposed to accurately predict rear axle damage. Simulated and the experimental results have shown the proposed method has relatively high prediction accuracy, and through comparison with traditional time series forecasting methods using the same parameters of vibration,it was found that the performance of artificial neural networks is better in forecasting accuracy.This study provides a new approach for predicting remaining gearing life.

关键词： damage prediction rear azle neural networks

作者: Yimin Shao Xiaoxia Li Chris K.Mechefske Ming J Zuo

作者单位: State Key Laboratory of Mechanical Transmission,Chongqing University,Chongqing,400044 Department of Mechanical and Materials Engineering,Queens University,Canada Department of Mechanical Engineering,University of Alberta,Canada

会议类型: 国际会议

会议名称: 2009 8th International Conference on Reliability,Maintainability and Safety(第八届中国国际可靠性、维修性、安全性会议)

会议地点: 成都

会议语种:英文

页码: 986-990

在线出版日期: 2009-08-24（万方平台首次上网日期，不代表论文的发表时间）