基于RF-SVR的燃油计量装置性能衰退检测和剩余寿命估计方法
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摘要
为了实现航空发动机燃油系统的安全状态监测和健康管理,开展了燃油系统性能衰退检测和剩余使用寿命估计方面的研究。以燃油系统燃油计量装置为例,分析了其主要的性能衰退模式,设计了基于电流-速度数据的健康指标(HIs)选取方案,并考虑环境及模型参数不确定性,进行模型不确定性仿真,基于健康数据与性能衰退数据间的马氏距离对部件性能衰退进行检测。提出了基于随机森林-支持向量回归(RFSVR)的剩余使用寿命(RUL)估计方法,利用通过RF特征选择优化的SVR模型实现部件RUL估计。最后基于某型民用涡扇发动机机械液压模型仿真数据对该方法进行了验证,结果表明:该方法的性能衰退检测虚警率及漏报率低于2%,RUL估计误差低于3%,可为航空发动机燃油系统的预测性维护提供参考。
In order to realize the safety state monitoring and health management of the aero-engine fuel system,the fuel system performance degradation detection and remaining useful life estimation was researched.Taking the fuel system fuel metering device as an example,the main performance degradation mode was analyzed.The health indicators(HIs)selection scheme based on current-speed data was designed.Considering the uncertainty of environment and model parameters,the model uncertainty simulation was carried out.The component performance degradation was detected based on the Mahalanobis distance between the healthy data and the performance degradative data.A remaining useful life(RUL)estimation method based on random forest-support vector regression(RF-SVR)was proposed.The component RUL estimation was realized by SVR model optimized by RF feature selection.Finally,the method was validated based on the simulation data of a certain type of civil turbofan engine mechanical hydraulic model.The results show that the performance of the method has a false alarm rate and a false negative rate of less than 2%,and the RUL estimation error is less than 3%.This provides a reference for predictive maintenance of aero engine fuel systems.
In order to realize the safety state monitoring and health management of the aero-engine fuel system,the fuel system performance degradation detection and remaining useful life estimation was researched.Taking the fuel system fuel metering device as an example,the main performance degradation mode was analyzed.The health indicators(HIs)selection scheme based on current-speed data was designed.Considering the uncertainty of environment and model parameters,the model uncertainty simulation was carried out.The component performance degradation was detected based on the Mahalanobis distance between the healthy data and the performance degradative data.A remaining useful life(RUL)estimation method based on random forest-support vector regression(RF-SVR)was proposed.The component RUL estimation was realized by SVR model optimized by RF feature selection.Finally,the method was validated based on the simulation data of a certain type of civil turbofan engine mechanical hydraulic model.The results show that the performance of the method has a false alarm rate and a false negative rate of less than 2%,and the RUL estimation error is less than 3%.This provides a reference for predictive maintenance of aero engine fuel systems.
引文
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[18]李艳军,张建,曹愈远,等.基于模糊信息粒化和优化SVM的航空发动机性能趋势预测[J].航空动力学报,2017,32(12):3022-3030.LI Yanjun,ZHANG Jian,CAO Yuyuan,et al.Forecasting of aero-engine performance trend based on fuzzy information granulation and optimized SVM[J].Journal of Aerospace Power,2017,32(12):3022-3030.(in Chinese)
[19]陈雄姿,于劲松,唐荻音,等.基于贝叶斯LS—SVR的锂电池剩余寿命概率性预测[J].航空学报,2013,34(9):2219-2229.CHEN Xiongzi,YU Jinsong,TANG Diyin,et al.Probabilistic residual life prediction for lithiumion batteries based on bayesian LS-SVR[J].Acta Aeronautica et Astronautica Sinica,2013,34(9):2219-2229.(in Chinese)
[20]ANDREA M,GIOVANNI J.Prognostic and health management system for fly-by-wire electro-hydraulic servo actuators for detection and tracking of actuator faults[J].Procedia CIRP,2017,59(1):116-121.
[2]VOLPONI A J.Gas turbine engine health management:past,presentand future trends[J].Journal of Engineering for Gas Turbines and Power,2014,136(5):051201.1-051201.20.
[3]姜彩虹,孙志岩,王曦.航空发动机预测健康管理系统设计的关键技术[J].航空动力学报,2009,24(11):2589-2594.JIANG Caihong,SUN Zhiyan,WANG Xi.Critical technologies for aero-engine prognostics and health management systems development[J].Journal of Aerospace Power,2009,24(11):2589-2594.(in Chinese)
[4]王骥超,郭迎清,王磊.新型高可靠性主燃油控制装置设计仿真研究[J].推进技术,2015,36(2):299-305.WANG Jichao,GUO Yingqing,WANG Lei.Research on design and simulation of a new type high-reliability mainfuel control unit[J].Journal of Propulsion Technology,2015,36(2):299-305.(in Chinese)
[5]LAMOUREUX B,MECHBAL N,MASSEJ.Selection and validation of health indicators in prognostics and health management system design[R].Nice,France:2013Conference on Control and Fault-Tolerant Systems,2013.
[6]赵志远,郭迎清,姜国龙,等.民用涡扇发动机燃油系统建模与仿真[R].成都:中国航空学会航空发动机自动控制专业学术交流会,2016.
[7]姜洁,李秋红,张高钱,等.基于NN-ELM的航空发动机燃油系统执行机构故障诊断[J].航空动力学报,2016,31(2):484-492.JIANG Jie,LI Qiuhong,ZHANG Gaoqian,et al.Fault diagnosis for actuators of aero-engine fuel system based on NN-ELM[J].Journal of Aerospace Power,2016,31(2):484-492.(in Chinese)
[8]姜洁,李秋红,张高钱,等.航空发动机燃油系统执行机构及其传感器故障诊断[J].航空动力学报,2015,30(6):1529-1536.JIANG Jie,LI Qiuhong,ZHANG Gaoqian,et al.Fault diagnosis for actuator and its sensor of aero-enging fuel system[J].Journal of Aerospace Power,2015,30(6):1529-1536.(in Chinese)
[9]芦海洋,王曦.基于卡尔曼滤波器的主燃油计量装置故障诊断[J].航空发动机,2017,43(2):17-22.LU HAIYANG,WANG Xi.Fault diagnosis of main fuel metering device based on Kalman filter[J].Aeroengine,2017,43(2):17-22.(in Chinese)
[10]LAMOUREUX B,MASSEJ,MECHBAL N.An approach to the health monitoring of the fuel system of a turbofan[R].Denver,CO,US:2012IEEE Conference on Prognostics and Health Management,2012.
[11]程涛,祁英,孟庆明.涡扇发动机主燃油控制系统建模与仿真研究[J].航空动力学报,1999,14(3):317-319.CHENG Tao,QI Ying,MENG Qingming.Modelling and simulation of main fuel control system for a turbofan engine[J].Journal of Aerospace Power,1999,14(3):317-319.(in Chinese)
[12]吴文斐,郭迎清,李睿,等.涡扇发动机液压机械主控制系统建模与仿真分析[J].航空发动机,2011,37(1):16-19.WU Wenfei,GUO Yingqing,LI Rui,et al.Modeling and simulation analysis of hydro-mechanical main control system for turbofan engine[J].Aeroengine,2011,37(1):16-19.(in Chinese)
[13]张治华,郭迎清.液压机械燃油调节器时变磨损可靠性分析[J].机床与液压,2008,36(6):173-175.ZHANG Zhihua,GUO Yingqing.The time-dependant wear reliability of the hydro-mechanical fuel regulator[J].Machine Tool and Hydraulics,2008,36(6):173-175.(in Chinese)
[14]SAHA B,GOEBEL K.Uncertainty management for diagnostics and prognostics of batteries using bayesian techniques[R].Big Sky,MT,US:2008IEEE Aerospace Conference,2008.
[15]GILES L.Understanding random forests:from theory to practice[D].Liege,Belgium:University of Liege,2014.
[16]张钰,陈珺,王晓峰,等.随机森林在滚动轴承故障诊断中的应用[J].计算机工程与应用,2018(6):100-104.ZHANG Yu,CHEN Jun,WANG Xiaofeng,et al.Application of random forest on rolling element bearings fault diagnosis[J].Computer Engineering and Applications,2018(6):100-104.(in Chinese)
[17]姚登举,杨静,詹晓娟.基于随机森林的特征选择算法[J].吉林大学学报,2014,44(1):137-141.YAO Dengju,YANG Jing,ZHAN Xiaojuan.Feature selection algorithm based on random forest[J].Journal of Jilin University,2014,44(1):137-141.(in Chinese)
[18]李艳军,张建,曹愈远,等.基于模糊信息粒化和优化SVM的航空发动机性能趋势预测[J].航空动力学报,2017,32(12):3022-3030.LI Yanjun,ZHANG Jian,CAO Yuyuan,et al.Forecasting of aero-engine performance trend based on fuzzy information granulation and optimized SVM[J].Journal of Aerospace Power,2017,32(12):3022-3030.(in Chinese)
[19]陈雄姿,于劲松,唐荻音,等.基于贝叶斯LS—SVR的锂电池剩余寿命概率性预测[J].航空学报,2013,34(9):2219-2229.CHEN Xiongzi,YU Jinsong,TANG Diyin,et al.Probabilistic residual life prediction for lithiumion batteries based on bayesian LS-SVR[J].Acta Aeronautica et Astronautica Sinica,2013,34(9):2219-2229.(in Chinese)
[20]ANDREA M,GIOVANNI J.Prognostic and health management system for fly-by-wire electro-hydraulic servo actuators for detection and tracking of actuator faults[J].Procedia CIRP,2017,59(1):116-121.
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