飞机液压系统故障诊断
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摘要
为有效诊断飞机液压系统故障,根据液压系统压力信号采用了熵权ABC-BP神经网络的故障诊断模型。模型先提取飞机液压系统压力信号的特征值,根据熵权法计算特征值信息熵,选取熵权值较大的作为神经网络的输入,同时利用人工蜂群优化BP神经网络,将BP神经网络的误差函数作为人工蜂群的适应度,选择适应度最优的个体参数作为神经网络的权值和阈值,不仅降低模型输入维度,还提高了诊断精度。最后建立了飞机起落架收放系统仿真模型进行仿真研究,结果表明该诊断模型具有较好的故障诊断效果,为飞机液压系统故障诊断提供一种新思路。
In order to diagnose the faults of aircraft hydraulic system effectively, a method based on entropy weight and ABC-BP neural network is proposed, which is according to the signal of hydraulic system pressure. In this model,extraction of eigenvalues of aircraft hydraulic system pressure signal is the first step, and then, it calculates eigenvalue information entropy according to entropy weight method, the bigger of results as the input of the neural network, and in this paper, BP neural network is optimized by artificial bee colony through replacing the artificial bee colony fitness with the error function of BP neural network, finally, selecting the best fitness individual parameters as the weights and thresholds of the neural network,this method not only reduces the input dimension of the model, but also improves the diagnostic accuracy.The simulation model of the landing gear retractable control system is established. The simulation results show that the diagnosis model has better fault diagnosis effect, and provides a new idea for the faults diagnosis of aircraft hydraulic system.
In order to diagnose the faults of aircraft hydraulic system effectively, a method based on entropy weight and ABC-BP neural network is proposed, which is according to the signal of hydraulic system pressure. In this model,extraction of eigenvalues of aircraft hydraulic system pressure signal is the first step, and then, it calculates eigenvalue information entropy according to entropy weight method, the bigger of results as the input of the neural network, and in this paper, BP neural network is optimized by artificial bee colony through replacing the artificial bee colony fitness with the error function of BP neural network, finally, selecting the best fitness individual parameters as the weights and thresholds of the neural network,this method not only reduces the input dimension of the model, but also improves the diagnostic accuracy.The simulation model of the landing gear retractable control system is established. The simulation results show that the diagnosis model has better fault diagnosis effect, and provides a new idea for the faults diagnosis of aircraft hydraulic system.
引文
[1]朱林,孔凡让,尹成龙,等.基于仿真计算的某型飞机起落架收放机构的仿真研究[J].中国机械工程,2007(1):26-29.
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[8]赵四军,王少萍,尚耀星.航空液压泵柱塞游隙增大故障诊断[J].北京航空航天大学学报,2010,36(3):261-264.
[9]陈灏,张梅军,黄杰,等.基于改进的EEMD方法与GA-SVM的液压系统泄漏故障诊断[J].液压与气动,2014(9):32-38.
[10] Yulmetyev R M,Emelyanova N A,Gafarov F M.Dynami-cal Shannon entropy and information Tsallis entropy incomplex systems[J].Physica A Statistical Mechanics&Its Applications,2004,341(28):649-676.
[11]李红卫,杨东升,孙一兰,等.智能故障诊断技术研究综述与展望[J].计算机工程与设计,2013,34(2):632-637.
[12]彭宇,刘大同.数据驱动故障预测和健康管理综述[J].仪器仪表学报,2014,35(3):481-495.
[13] Karaboga D,Ozturk C.A novel clustering approach:Artificial Bee Colony(ABC)algorithm[J].Applied SoftComputing,2011,11(1):652-657.
[14] Nourani E,Rahmani A M,Navin A H.Forecasting stockprices using a hybrid artificial bee colony based neuralnetwork[C]//International Conference on Innovation Man-agement and Technology Research,2012:486-490.
[15] Gu H,Xiao C,Liu Y,et al.AMESim used in dynamicssimulation on hydraulic milling machine[C]//InternationalConference on Communication Software and Networks,2011:620-623.
[16] Zhang J,Wu Y,Tang H M,et al.Study on dynamiccharacteristic in hydraulic system based on bypassmethod by the AMESim[J].Applied Mechanics&Mate-rials,2015,703:298-302.
[2]范士娟,杨超.液压系统故障智能诊断技术现状与发展趋势[J].液压与气动,2010(3):22-26.
[3]和麟,梁丽嫒,马存宝.飞机起落架液压收放系统多故障仿真与健康评估[J].西北工业大学学报,2016,34(6):990-995.
[4]崔建国,林泽力,陈希成,等.飞机液压系统健康状态综合评估技术研究[J].控制工程,2014,21(3).
[5]赵四军,王少萍,尚耀星.飞机液压泵源预测与健康管理系统[J].北京航空航天大学学报,2010,36(1):14-17.
[6]何庆飞,陈桂明,陈小虎,等.基于改进灰色神经网络的液压泵寿命预测[J].中国机械工程,2013,24(4):500-506.
[7]吴亚锋,郭军.基于AMESim的飞机液压系统仿真技术的应用研究[J].沈阳工业大学学报,2007,29(4):368-371.
[8]赵四军,王少萍,尚耀星.航空液压泵柱塞游隙增大故障诊断[J].北京航空航天大学学报,2010,36(3):261-264.
[9]陈灏,张梅军,黄杰,等.基于改进的EEMD方法与GA-SVM的液压系统泄漏故障诊断[J].液压与气动,2014(9):32-38.
[10] Yulmetyev R M,Emelyanova N A,Gafarov F M.Dynami-cal Shannon entropy and information Tsallis entropy incomplex systems[J].Physica A Statistical Mechanics&Its Applications,2004,341(28):649-676.
[11]李红卫,杨东升,孙一兰,等.智能故障诊断技术研究综述与展望[J].计算机工程与设计,2013,34(2):632-637.
[12]彭宇,刘大同.数据驱动故障预测和健康管理综述[J].仪器仪表学报,2014,35(3):481-495.
[13] Karaboga D,Ozturk C.A novel clustering approach:Artificial Bee Colony(ABC)algorithm[J].Applied SoftComputing,2011,11(1):652-657.
[14] Nourani E,Rahmani A M,Navin A H.Forecasting stockprices using a hybrid artificial bee colony based neuralnetwork[C]//International Conference on Innovation Man-agement and Technology Research,2012:486-490.
[15] Gu H,Xiao C,Liu Y,et al.AMESim used in dynamicssimulation on hydraulic milling machine[C]//InternationalConference on Communication Software and Networks,2011:620-623.
[16] Zhang J,Wu Y,Tang H M,et al.Study on dynamiccharacteristic in hydraulic system based on bypassmethod by the AMESim[J].Applied Mechanics&Mate-rials,2015,703:298-302.