基于退化特征相似性的航空发动机寿命预测
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摘要
针对航空发动机结构复杂、性能退化参数众多、寿命预测精度低等问题,提出了一种基于退化特征相似性的寿命预测方法。首先通过基于Relief算法的退化特征筛选、基于主成分分析(principal component analysis,PCA)的特征提取和基于核函数的特征平滑,提取低维正交多变量退化特征;然后进行特征的相似性匹配,寻找与当前样本特征片段最相似的一组历史样本中的特征片段集合,将这些片段对应的RUL信息融合并采用密度加权方法得到当前样本的寿命预测估计值;最后通过美国国家航空航天局(national aeronautics and space administration,NASA)提供的航空涡轮扇发动机仿真数据集验证了该方法的有效性,其寿命预测性能高于现有几种代表性方法。
Aircraft engines are with highly complex structures,a large number of performance parameters monitored and low remaining useful life(RUL)prediction accuracy in their degradation processes.A method based on the similarity of degradation characteristics is proposed for predicting the RUL of aircraft engines.Firstly,the Relief algorithm is used for feature selection,principal component analysis is used for feature extraction,and the Kernel algorithm is used for feature trajectory smoothing.Through this,a few low-dimensional orthogonal degradation characteristics are obtained for RUL prediction.Then,a similarity-based matching algorithm is adopted to find out a group of similar degradation segments in the referenced samples,and the corresponding RUL information of these degradation segments are integrated via a density weighting method to obtain the final RUL prediction of the current sample.Finally,the proposed method is evaluated by using the turbofan engine degradation simulation data announced by national aeronautics and space administration(NASA),and the results can show its superiority compared with several popular RUL prediction methods.
Aircraft engines are with highly complex structures,a large number of performance parameters monitored and low remaining useful life(RUL)prediction accuracy in their degradation processes.A method based on the similarity of degradation characteristics is proposed for predicting the RUL of aircraft engines.Firstly,the Relief algorithm is used for feature selection,principal component analysis is used for feature extraction,and the Kernel algorithm is used for feature trajectory smoothing.Through this,a few low-dimensional orthogonal degradation characteristics are obtained for RUL prediction.Then,a similarity-based matching algorithm is adopted to find out a group of similar degradation segments in the referenced samples,and the corresponding RUL information of these degradation segments are integrated via a density weighting method to obtain the final RUL prediction of the current sample.Finally,the proposed method is evaluated by using the turbofan engine degradation simulation data announced by national aeronautics and space administration(NASA),and the results can show its superiority compared with several popular RUL prediction methods.
引文
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[21]SAXENA A,KAI G,SIMON D,et al.Damage propagation modeling for aircraft engine run-to-failure simulation[C]∥Proc.of the IEEE International Conference on Prognostics and Health Management,2008:1-9.
[2]王华伟,吴海桥.基于信息融合的航空发动机剩余寿命预测[J].航空动力学报,2012,27(12):2749-2755.WANG H W,WU H Q.Remaining useful life prediction for aircraft engine based on information fusion[J].Journal of Aerospace Power,2012,27(12):2749-2755.
[3]胡昌华,施权,司小胜,等.数据驱动的寿命预测和健康管理技术研究进展[J].信息与控制,2017,46(1):72-82.HU C H,SHI Q,SI X S,et al.Data-driven life prediction and health management:state of the art[J].Information and Control,2017,46(1):72-82.
[4]JAVED K,GOURIVEAU R,ZERHOUNI N.State of the art and taxonomy of prognostics approaches,trends of prognostics applications and open issues towards maturity at different technology readiness levels[J].Mechanical Systems&Signal Processing,2017,94:214-236.
[5]彭宝华,周经伦,孙权,等.基于退化与寿命数据融合的产品剩余寿命预测[J].系统工程与电子技术,2011,33(5):1073-1078.PENG B H,ZHOU J L,SUN Q,et al.Residual lifetime prediction of products based on fusion of degradation data and lifetime data[J].Systems Engineering and Electronics,2011,33(5):1073-1078.
[6]朱磊,左洪福,蔡景.基于Wiener过程的民用航空发动机性能可靠性预测[J].航空动力学报,2013,28(5):1006-1012.ZHU L,ZUO H F,CAI J.Performance reliability prediction for civil aviation aircraft engine based on Wiener process[J].Journal of Aerospace Power,2013,28(5):1006-1012.
[7]WANG X,BALAKRISHNAN N,GUO B.Remaining useful life estimation based on a generalized Wiener degradation process[J].Reliability Engineering&System Safety,2014,124(124):13-23.
[8]PAN D,LIU J B,CAO J.Remaining useful life estimation using an inverse Gaussian degradation model[J].Neurocomputing,2016,185(C):64-72.
[9]WANG P F,YOUN B D,HU C.A generic probabilistic framework for structural health prognostics and uncertainty management[J].Mechanical Systems and Signal Processing,2012,28:622-637.
[10]ZHOU H,HUANG J,LU F.Reduced kernel recursive least squares algorithm for aero-engine degradation prediction[J].Mechanical Systems&Signal Processing,2017,95:446-467.
[11]NIETO P J G,GARCíA-GONZALO E,LASHERAS F S,et al.Hybrid PSO-SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability[J].Reliability Engineering&System Safety,2015,138:219-231.
[12]JAVED K,GOURIVEAU R,ZERHOUNI N.A new multivariate approach for prognostics based on extreme learning machine and fuzzy clustering[J].IEEE Trans.on Cybernetics,2015,45(12):2626-2639.
[13]LIAO L,KOTTIG F.Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems,and an application to battery life prediction[J].IEEE Trans.on Reliability,2014,63(1):191-207.
[14]YOU M Y,MENG G.A framework of similarity-based remaining useful life prediction approaches using degradation histories with failure,preventive maintenance,and suspension events[J].IEEE Trans.on Reliability,2013,62(1):127-135.
[15]WANG T.Trajectory similarity based prediction for remaining useful life estimation[J].Dissertations&ThesesGradworks,2010.
[16]LAM J,SANKARARAMAN S,STEWART B.Enhanced trajectory based similarity prediction with uncertainty quantification[C]∥Proc.of the Annual Conference of the Prognostics and Health Management Society,2014:1-12.
[17]邓森,景博,周宏亮.基于灰色时序组合模型的产品实时寿命预测方法[J].系统工程与电子技术,2014,36(4):802-808.DENG S,JING B,ZHOU H L.Real-time lifetime prediction method for products based on grey time series combined forecasting model[J].Systems Engineering and Electronics,2014,36(4):802-808.
[18]ZHANG Q,TSE P W,WAN X,et al.Remaining useful life estimation for mechanical systems based on similarity of phase space trajectory[J].Expert Systems with Applications,2015,42(5):2353-2360.
[19]KIRA K,RENDELL L A.A practical approach to feature selection[J].Machine Learning Proceedings,1992,48(1):249-256.
[20]WANG T,YU J,SIEGEL D,et al.A similarity-based prognostics approach for remaining useful life estimation of engineered systems[C]∥Proc.of the IEEE International Conference on Prognostics and Health Management,2008:1-6.
[21]SAXENA A,KAI G,SIMON D,et al.Damage propagation modeling for aircraft engine run-to-failure simulation[C]∥Proc.of the IEEE International Conference on Prognostics and Health Management,2008:1-9.