Fault Dignosis of Rolling Bearing Based on Time Domain Parameters

摘要：

The rolling bearing is the common component in machinery. Its running state will influence the performance of the whole machine directly. In this paper we put forward a feature extraction method of fault diagnosis of rolling bearing. After the vibration signals of the rolling bearing are analysed and processed, the feature parameters which represent operating state of the rolling bearing are extracted, and then are inputted to the BP neural network to train the network with BP algorithm by processing of normalization. Good rolling bearings and bad rolling bearings can be identified with this network. The simulation result shows that the method presented in this paper is practical and effective.

关键词： Rolling Bearing Fault Diagnosis Feature Parameter BP Neural Network

作者: Jibin Chang Taifu Li Qiang Luo

作者单位: Chongqing University of Science and Technology, School of Electronic & Information Engineering, Chon Chongqing University of Science and Technology, College of Mechanical & Dynamic Engineering, Chongqi

会议类型: 国际会议

会议名称: The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

会议地点: 徐州

会议语种:英文

页码: 2215-2218

在线出版日期: 2010-05-26（万方平台首次上网日期，不代表论文的发表时间）