神经网络在机车测速传感器故障诊断中的应用  

Application of RBF Neural Network to Locomotive Speed Sensor Fault Diagnosis

作　　者：谢彬[1] 李国宁[1] 冯涛[1] 李博 

机构地区：[1]兰州交通大学自动化与电气工程学院,甘肃兰州730070 [2]北京铁路信号有限公司,北京102613

出　　处：《机车电传动》2012年第6期84-87,共4页Electric Drive for Locomotives

摘　　要：提出了采用径向基(RBF)神经网络设计一种新型的机车测速传感器故障诊断方法。以光电式测速传感器的常见故障为模型建立RBF神经网络预测器,通过对预测器进行在线训练和实时检测来判断测速传感器是否发生故障,并对故障传感器提出决策方法和进行数据重构。仿真结果表明,该方法能够高精度地模拟传感器的输出特性,快速有效地进行测速传感器的故障诊断。An locomotive speed sensor fault diagnosis method based on Radial Basis Function （RBF） neural network was presented. RBF neural network predictor was built by taking common faults of photoelectric speed sensor as model. Through on-line training and realtime diagnosis, it was determined whether sensor was faulted, and then diagnostic decision methods were put forward and fault date of sensor was reconstructed. The simulation results show that dynamic characteristics of sensor are accurately simulated, which can rapidly and effectively realize speed sensor fault diagnosis.

关 键 词：测速传感器 RBF神经网络 故障诊断 预测器 

分 类 号：U269-322[机械工程—车辆工程] TP212[交通运输工程—载运工具运用工程]