Fault Diagnosis for Gear Pump Based on Feature Fusion of Vibration Signal

摘要：

　　Information fusion arises in a surprising number of fault diagnosis applications.In this paper,common faults are designed in the experiment according to the gear pump vibration mechanism.Fault signal is collected from vibration sensors of different positions,and wavelet packet energy percentage and RMS are extracted as features of the signal.RBF neural network is adopted to fuse thiese features which are used to learn and train the network.The testing results prove that this approach possesses higher diagnostic precision and better diagnostic effect than single signal fault diagnosis.

关键词： fault diagnosis wavelet packet energy percentage neural network feature fusion

作者: Xiliang Liu Guiming Chen Fangxi Li Qian Zhang Zhenqi Dong

作者单位: Xian Research Institute of Hi-Tech Xian, China

会议类型: 国际会议

会议名称: 2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering & The 3rd International Conference on Maintenance Engineering (2012质量,可靠性,风险,维修性及安全性工程国际会议(QR2MSE 2012 & ICME 2012))

会议地点: 成都

会议语种:英文

页码: 708-711

在线出版日期: 2012-06-15（万方平台首次上网日期，不代表论文的发表时间）