[1]周 博,吕 琛,王 轩,等.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(02):149.[doi:10.14177/j.cnki.32-1397n.2016.40.02.004]
 Zhou Bo,Lv Chen,Wang Xuan,et al.Adaptive fault detection based on GRNN observer for hydraulic actuator system[J].Journal of Nanjing University of Science and Technology,2016,40(02):149.[doi:10.14177/j.cnki.32-1397n.2016.40.02.004]
点击复制

基于GRNN观测器的液压作动器系统自适应 故障检测
分享到:

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
40卷
期数:
2016年02期
页码:
149
栏目:
出版日期:
2016-04-30

文章信息/Info

Title:
Adaptive fault detection based on GRNN observer for hydraulic actuator system
文章编号:
1005-9830(2016)02-0149-07
作者:
周 博1吕 琛12王 轩1田 野1秦维力1
北京航空航天大学 1.可靠性与系统工程学院; 2.可靠性与环境工程技术重点实验室,北京 100191
Author(s):
Zhou Bo1Lv Chen12Wang Xuan1Tian Ye1Qin Weili1
1.School of Reliability and Systems Engineering; 2.Science & Technology Laboratory on Reliability & Environmental Engineering,Beihang University,Beijing 100191,China
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
Keywords:
hydraulic actuators general regression neural network observers adaptive fault detection
分类号:
TP277
DOI:
10.14177/j.cnki.32-1397n.2016.40.02.004
摘要:
针对液压作动器系统观测器检测诊断技术较少的状况,该文提出一种基于广义回归神经网络(General regression neural network,GRNN)观测器的液压作动器系统自适应故障检测方法,GRNN神经网络的学习速度较快,能大幅提高训练效率。针对环境噪声和随机干扰等因素的影响,引入自适应阈值来降低检测虚警率。首先使用液压作动器系统正常运行时的数据训练神经网络,用训练好的网络对采集的数据进行故障检测,判断液压作动系统是否发生故障。液压作动器系统3种典型故障模式的仿真数据验证了该文方法的有效性。实验结果表明,该方法能够有效检测出液压作动器系统的故障状态。
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 effectively.

参考文献/References:

[1] 王可,夏立群.基于模糊逻辑的作动器故障诊断方法研究[J].机床与液压,2010,38(15):126-128.
Wang Ke,Xia Liqun.Actuator fault diagnosis based on fuzzy logic[J].Machine Tool & Hydraulics,2010,38(15):126-128.
[2]柳志娟,李清,柳先辉.基于强跟踪多模型估计器的作动器故障诊断[J].清华大学学报,2012,52(5):642-647.
Liu Zhijuan,Li Qing,Liu Xianhui.Actuator fault diagnostics based on a strong-tracking multiple model estimator[J].Journal of Tsinghua University,2012,52(5):642-647.
[3]Du Jun,Wang Shaoping,Zhang Haiyan.Layered clustering multi-fault diagnosis for hydraulic piston pump[J].Mechanical Systems and Signal Processing,2013,36(2):487-504.
[4]Zhang Guopeng,Wang Bo.Fault diagnosis of flying control system servo actuator based on Elman neural network[J].The Tenth International Conference on Electronic Measurement & Instruments,2011,4:46-49.
[5]宋玉琴,章卫国,刘小雄.基于RBF神经网络观测器飞控系统故障诊断[J].计算机仿真,2010,27(3):85-93.
Song Yuqin,Zhang Weiguo,Liu Xiaoxiong.Fault diagnosis based on RBF neural network observer in flight control system[J].Computer Simulation,2010,27(3):85-93.
[6]贺湘宇,何清华,蒋 苹,等.基于动态 GRNN 模型的挖掘机液压系统故障检测[J].中国工程机械学报,2010,8(3):335-339.
He Xiangyu,He Qinghua,Jiang Ping,et al.Dynamic GRNN-based fault detection on excavator hydraulic system[J].Chinese Journal of Construction Machinery,2010,8(3):335-339.
[7]袁颖,周爱红,李治广.基于GRNN神经网络的桁架结构损伤识别的两步法[J].建筑科学,2013,29(9):48-52.
Yuan Ying,Zhou Aihong,Li Zhiguang.Two-step method of damage identification for truss structure based on generalized regression neural network[J].Building Science,2013,29(9):48-52.
[8]Yan Xiaomo,Tian Bailing,Wang Hong.An adaptive observer-based fault detection and diagnosis for nonlinear systems with sensor and actuator faults[C]//Proceedings of the 2015 International Conference on Advanced Mechatronic Systems.Beijing,China:ICAMechS,2015:491-496.
[9]周敏,李世玲.广义回归神经网络在非线性系统建模中的应用[J].计算机测量与控制,2007,15(9):1189-1191.
Zhou Min,Li Shiling.Application of GRNN and uniform design to nonlinear system modeling[J].Computer Measurement & Control,2007,15(9):1189-1191.
[10]Loukil R,Chtourou M,Damak T,et al.Fault diagnosis and isolation of a complex system using a neural network observer[J].International Journal of Automation & Control,2013(3):147-165.
[11]刘大伟.液压伺服系统性能退化评估与预测技术研究[D].北京:北京航空航天大学可靠性与系统工程学院,2013.

备注/Memo

备注/Memo:
收稿日期:2015-09-23 修回日期:2016-01-11
基金项目:国防技术基础项目(Z132013B002)
作者简介:周博(1991-),男,硕士生,主要研究方向:故障诊断、预测与健康管理,E-mail:zhoubozhoubo1991@163.com; 通讯作者:吕琛(1974-),男,博士,教授,主要研究方向:故障诊断、预测与健康管理,E-mail:luchen@buaa.edu.cn。
引文格式:周博,吕琛,王轩,等.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,2016,40(2):149-155.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2016-04-30