基于监督流形学习的航空发动机振动故障诊断方法

推进技术 ›› 2017, Vol. 38 ›› Issue (5): 1147-1154.

基于监督流形学习的航空发动机振动故障诊断方法

张赟¹,杨栋²,斯彦刚²,方旭萌²

海军航空工程学院飞行器工程系，山东烟台 264001,中国人民解放军 92074部队，浙江宁波 315000,中国人民解放军 92074部队，浙江宁波 315000,中国人民解放军 92074部队，浙江宁波 315000

发布日期:2021-08-15
作者简介:张赟，男，博士，讲师，研究领域为航空发动机测试与故障诊断。
基金资助:
国家自然科学基金(51505492)；山东省自然科学基金(ZR2013EEQ001)；“泰山学者”建设工程专项经费资助。

Aero-Engine Vibration Fault Diagnosis Based on Supervised Manifold Learning

Department of Airborne Vehicle Engineering，Naval Aeronautical Engineering Institute，Yantai 264001，China,92074 unit of PLA，Ningbo 315000，China,92074 unit of PLA，Ningbo 315000，China and 92074 unit of PLA，Ningbo 315000，China

Published:2021-08-15

摘要/Abstract

摘要： 航空发动机故障诊断中一个有挑战性的难题就是如何处理具有高维数、非线性化特点的故障数据，传统模式识别方法很难发现这类数据集的真实结构，导致故障诊断准确性不高。针对这一问题，将一种新兴的非线性维数约简技术——流形学习引入航空发动机振动故障诊断，提出基于监督流形学习理论的航空发动机特征提取与识别方法。该方法首先采用最近兴起的监督局部线性嵌入流形学习算法对蕴含在高维振动故障数据中不同故障的流形特征进行学习，映射到低维嵌入空间以实现故障的特征提取，在降维后的流形特征空间中构造分类器实现故障识别。利用航空发动机转子故障数据对方法的有效性进行了验证，结果表明，该方法显著提高了故障诊断性能，克服了传统的模式识别方法PCA和LDA的不足，并且在训练样本数为每类100的条件下，该方法的平均故障诊断正确率比PCA和LDA分别高出2.93%和7.20%。

关键词: 航空发动机；振动信号；监督流形学习；故障诊断

Abstract: How to deal with the high-dimensional and nonlinear data is a challenging problem for aero-engine fault diagnosis. The conventional pattern recognition methods usually fail to discover the underlying structure of such data sets，leading to low accuracy of fault diagnosis. Thus by introducing the new nonlinear dimensionality reduction technique into aero-engine vibration fault diagnosis，an aero-engine fault feature extraction and recognition approach based on manifold learning is proposed. The approach firstly performs the recently proposed manifold learning algorithm supervised locally linear embedding on the high-dimensional fault signal samples to learn the intrinsic embedded multiple manifold features for achieving fault feature extraction. And the classifier is constructed for fault recognition in the reduced manifold feature space. The fault rotor data of aero-engine is employed to validate the proposed approach. The experiment results show that the proposed approach obviously improves the fault classification performance and outperform the other traditional approaches such as Principal Component Analysis(PCA)and Linear Discriminate Method(LDA). Comparing to PCA and LDA，the average diagnosis accuracies of the approach increase by 2.93% and 7.20% respectively as the training sample size is 100 per class.

Key words: Aeroengine；Vibration signal；Supervised manifold learning；Fault diagnosis

张赟,杨栋,斯彦刚,方旭萌. 基于监督流形学习的航空发动机振动故障诊断方法[J]. 推进技术, 2017, 38(5): 1147-1154.

ZHANG Yun¹,YANG Dong²,SI Yan-gang²,FANG Xu-meng². Aero-Engine Vibration Fault Diagnosis Based on Supervised Manifold Learning[J]. Journal of Propulsion Technology, 2017, 38(5): 1147-1154.

参考文献

[1] 费成巍, 白广忱. 基于WCFSE-FSVM的转子振动故障诊断方法[J]. 推进技术, 2013, 34(9): 1266-1271. (FEI Cheng-wei, BAI Guang-chen. Rotor Vibration Fault Diagnosis Method Based on WCFSE-FSVM[J]. Journal of Propulsion Technology, 2013, 34(9): 1266-1271.)
[2] 徐启华, 师军, 耿帅. 应用快速多分类SVM的航空发动机故障诊断方法[J]. 推进技术, 2012, 33(6): 962-967. (XU Qi-hua, SHI Jun, GENG Shuai. Aero-Engine Fault Diagnosis by a New Fast Multi-Class Support Vector Algorithm[J]. Journal of Propulsion Technology, 2012, 33(6): 962-967.)
[3] 张赟, 李本威, 贾舒宜, 等. 航空发动机混叠振动信号的欠定盲源分离方法[J]. 推进技术, 2014, 35(4): 553-558. (ZHANG Yun, LI Ben-wei, JIA Shu-yi, et al. Underdetermined Blind Separation of Aeroengine Vibration Signal Mixtures[J]. Journal of Propulsion Technology, 2014, 35(4): 553-558.)
[4] Jolliffe I T. Principal Component Analysis[M]. New York: Springer-Verlag, 2002.
[5] Weixiang Sun, Jin Chen, Jiaqing Li. Decision Tree and PCA-Based Fault Diagnosis of Rotating Machinery[J]. Mechanical Systems and Signal Processing, 2007, 21(3): 1300-1317.
[6] 杨帆, 胡金海, 等. 主元分析方法在航空发动机故障检测与诊断中的应用[J]. 机械科学与技术, 2008, 27(3): 330-333.
[7] Fukunaga K. Introduction to Statistical Pattern Recognition[M]. USA: Academic Press, 1990.
[8] Shi K, Liu S, Zhang H, et al. Kernel Local Linear Discriminate Method for Dimensionality Reduction and Its Application in Machinery Fault Diagnosis[J]. Shock and Vibration, 2014,(1): 2-11.
[9] Martinez A M, Kak A C. PCA versus LDA[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(2): 228-233.
[10] Scholk?pf B, Smola A, Müller K R. Nonlinear Component Analysis as a Kernel Eigenvalue Problem[J]. Neural Computation, 1998, 10(5): 1299-1319.
[11] Achmad Widodo, Bo-Suk Yang. Application of Nonlinear Feature Extraction and Support Vector Machines for Fault Diagnosis of Induction Motors[J]. Expert Systems with Applications, 2007, 33: 241-250.
[12] Roweis S T, Saul L K. Nonlinear Dimensionality Reduction by Locally Linear Embedding[J]. Science, 2000, 290: 2323-2326.
[13] Tenenbaum J B, Silva V D, Langford J C. A Global Geometric Framework for Nonlinear Dimensionality Reduction[J]. Science, 2000, 290: 2319-2323.
[14] Belkin M, Niyogi P. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation[J]. Neural Computation, 2003, 15(6): 1373-1396.
[15] Genaro D S, German C D, Jose C. Locally Linear Embedding based on Correntropy Measure for Visualization and Classification[J]. Neurocomputing, 2012, 80: 19-30.
[16] Zhang J P, Li S Z, Wang J. Manifold Learning and Applications in Recognition[J]. Springer, 2005, 168: 281-300.
[17] Jing Han, Jiang Yue, Yi Zhang, et al. Kernel Maximum Likelihood Scaled Locally Linear Embedding for Night Vision Images[J]. Optics & Laser Technology, 2014, 56: 290-298.
[18] Ridder D D, Duin R P W. Locally Linear Embedding for Classification[R]. Technical Report PH-2002-01, Delft University of Technology, 2002.
[19] Kayo O. Locally Linear Embedding Algorithm Extensions and Applications[M]. Finland: Faculty of Technology, University of Oulu, 2006.
[20] Horn R A, Johnson C R. Matrix Analysis[M]. Cambridge: Cambridge University Press, 1990.
[21] Tenenbaum J B, Silva V D, Langford J C. A Global Geometric Framework for Nonlinear Dimensionality Reduction[J]. Science, 2000, 290: 2319-2323.
[22] Quansheng Jiang, Minping Jia, Jianzhong Hua, et al. Machinery Fault Diagnosis Using Supervised Manifold Learning[J]. Mechanical Systems and Signal Processing, 2009, 23: 2301-2311.
[23] Li F, Tang B P, Yang R S. Rotating Machine Fault Diagnosis Using Dimension Reduction with Linear Local Tangent Space Alignment[J]. Measurement, 2013, 46: 2525-2539.　　　　　　　　　　　　　*　收稿日期:2016-01-14；修订日期:2016-04-05。基金项目:国家自然科学基金(51505492)；山东省自然科学基金(ZR2013EEQ001)；“泰山学者”建设工程专项经费资助。作者简介:张赟,男,博士,讲师,研究领域为航空发动机测试与故障诊断。E-mail: hjhy_zy@126.com(编辑:张荣莉)