南京理工大学学报（自然科学版）2016，Vol.40Issue(2)：149-155,7.DOI:10.14177/j.cnki.32-1397n.2016.40.02.004
基于GRNN观测器的液压作动器系统自适应故障检测
Adaptive fault detection based on GRNN observer for hydraulic actuator system
摘要
Abstract
In view of that the technology detecting the fault of the hydraulic actuator systems using observer is still limited,an adaptive failure detection method based on the general regression neural network(GRNN) observer for the hydraulic actuator system is presented here. The faster learning speed of the GRNN neural network makes training much more efficient. Because of the influence of environmental noise and random interference,the adaptive threshold is introduced to reduce the false alarm rate of detection. The data of the hydraulic actuator system in normal operation is used to train the neural network,then the trained neural network for the diagnosis of the collected data is used to judge whether the hydraulic actuator system fails. The three typical types of faults of the hydraulic actuator system are used to verify the effectiveness of this method. The experimental analysis results show that the proposed method can detect the fault condition of the hydraulic actuator system effec-tively.关键词
液压作动器/广义回归神经网络/观测器/自适应故障检测Key words
hydraulic actuators/general regression neural network/observers/adaptive fault detection分类
信息技术与安全科学引用本文复制引用
周博,吕琛,王轩,田野,秦维力..基于GRNN观测器的液压作动器系统自适应故障检测[J].南京理工大学学报（自然科学版）,2016,40(2):149-155,7.基金项目
国防技术基础项目(Z132013B002) （Z132013B002）
