基于SVM和SNN的航空发动机气路故障诊断

王修岩; 李萃芳; 高铭阳; 李宗帅

doi:10.13224/j.cnki.jasp.2014.10.029

基于SVM和SNN的航空发动机气路故障诊断

doi: 10.13224/j.cnki.jasp.2014.10.029

1. 中国民航大学航空自动化学院, 天津 300300;
2. 北京航空航天大学仪器科学与光电工程学院, 北京 100191

基金项目:

中央高校基本科研业务费中国民航大学专项（ZXH2010C002）

详细信息

作者简介:
王修岩（1965- ），男，吉林农安人，教授、硕士生导师，博士，主要从事航空发动机故障诊断和控制理论研究.

中图分类号: V233.7;TP181
计量
- 文章访问数: 1327
- HTML浏览量: 5
- PDF量: 794
- 被引次数: 0
出版历程
- 收稿日期: 2013-06-14
- 刊出日期: 2014-10-28

Fault diagnosis of aero-engine gas path based on SVM and SNN

1. School of Aeronautical Automation, Civil Aviation University of China, Tianjin 300300, China;
2. School of Instrumentation Science and Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

摘要

摘要: 为了区分航空发动机气路故障诊断过程中出现的相似故障，提高诊断准确率，提出了一种支持向量机（SVM）和协同神经网络（SNN）相结合的故障诊断方法.首先利用参数优化后的SVM对测量数据进行初步故障诊断分类，对诊断结果进行分析统计，得出难以区分的相似故障类型，并根据SNN对这些相似故障进一步地区分判断，最后根据实际数据对此故障模型进行仿真.结果显示：基于SVM的初步故障诊断准确率达到96%；而经过SNN进一步地相似故障区分后，诊断准确率提升到100%.
- 航空发动机 /
- 故障诊断 /
- 气路 /
- 支持向量机 /
- 协同神经网络
Abstract: In order to distinguish similar faults of aero-engine gas path fault diagnosis and improve the diagnostic accuracy, a fault diagnosis method based on support vector machine(SVM) and synergetic neural network(SNN) was put forward. Firstly, the SVM after being optimized was used to diagnose and classify the faults preliminarily form measured data, and the diagnosis results were analyzed to obtain indistinguishable similar faults, then the SNN was introduced to distinguish similar faults and further determine corresponding fault model, finally this fault model was simulated based on actual data. The experimental results show that the preliminary fault diagnosis accuracy based on SVM is 96%, and after further distinguishing similar faults through SNN, the accuracy is increased to 100%.
- aero-engine /
- fault diagnosis /
- gas path /
- support vector machine /
- synergetic neural network

HTML

参考文献(16)

[1]	李一波,张光明,蒋丽英.航空发动机气路故障诊断技术研究现状[J].燃气轮机技术,2009,22(3):10-15. LI Yibo,ZHANG Guangming,JIANG Liying.Research of aero-engine gas path fault diagnosis technology[J].Gas Turbine Technology,2009,22(3):10-15.(in Chinese)
[2]	邓明,金业壮.航空发动机故障诊断[M].北京:北京航空航天大学出版社,2012.
[3]	张学工.关于统计学习理论与支持向量机[J].自动化学报,2000,26(1):32-42. ZHANG Xuegong.Introduction to statistical learning theory and support vector machines[J].Acta Automatica Sinica,2000,26(1):32-42.(in Chinese)
[4]	高隽.人工神经网络原理及仿真实例[M].北京:机械工业出版社,2003.
[5]	易辉,宋晓峰,姜斌,等.基于结点优化的决策导向无环图支持向量机及其在故障诊断中的应用[J].自动化学报,2010,36(3):427-432. YI Hui,SONG Xiaofeng,JIANG Bin,et al.Support vector machine based on nodes refined decision directed a cyclic graph and its application to fault diagnosis[J].Acta Automatica Sinica,2010,36(3):427-432.(in Chinese)
[6]	徐启华,师军.基于支持向量机的航空发动机故障诊断[J].航空动力学报,2005,20(2):298-302. XU Qihua,SHI Jun.Aero-engine fault diagnosis based on support vector machine[J].Journal of Aerospace Power,2005,20(2):298-302.(in Chinese)
[7]	陈果.基于遗传算法的支持向量机分类器模型参数优化[J].机械科学与技术,2007,26(3):347-350. CHEN Guo.Optimizing the parameters of support vector machine's classifier model based on genetic algorithm[J].Mechanical Science and Technology,2007,26(3):347-350.(in Chinese)
[8]	邵信光,杨慧中,陈刚.基于粒子群优化算法的支持向量机参数选择及其应用[J].控制理论与应用,2006,23(5):347-350. SHAO Xinguang,YANG Huizhong,CHEN Gang.Parameters selection and application of support vector machines based on particle swarm optimization algorithm[J].Control Theory and Applications,2006,23(5):347-350.(in Chinese)
[9]	刘秉瀚,王伟智,方秀端.协同模式识别方法综述[J].系统工程与电子技术,2003,25(6):758-762. LIU Binghan,WANG Weizhi,FANG Xiuduan.A survey of the synergetic algorithm for pattern recognition[J].Systems Engineering and Electronics,2003,25(6):758-762.(in Chinese)
[10]	Olemskoi A I,Khomenko A V.Synergetic theory for a jamming transition in traffic flow[J].Physical Review E:Statistical,Nonlinear,and Soft Matter Physics,2001,63(3):36116.1-36116.4.
[11]	Cerra D,Muller R,Reinartz P.A classification algorithm for hyperspectral images based on synergetics theory[J].Transactions on Geoscience and Remote Sensing,2013,51(5):2887-2898.
[12]	Nakagawa M.A circularly connected synergetic neural network[J].IEICE Transactions on Fundamentals of Electronics,Communications and Computer Sciences,2000,E83-A(5):909-922.
[13]	彭淑宏.航空发动机气路故障诊断技术研究[D].上海:上海交通大学,2012. PENG Shuhong.Study on gas path fault diagnosis technology of aeroengine[D].Shanghai:Shanghai Jiao Tong University,2012.(in Chinese)
[14]	张凤鸣,惠晓滨.航空装备故障诊断学[M].北京:国防工业出版社,2010.
[15]	董火明,高隽,陈定国,等.协同神经网络聚类型学习算法[J].合肥工业大学学报,2002,25(4):492-495. DONG Huoming,GAO Jun,CHEN Dingguo,et al.Cluster learning algorithm of synergetic neural network[J].Journal of Hefei University of Technology,2002,25(4):492-495.(in Chinese)
[16]	潘连荣.基于协同学理论的变压器故障诊断研究[D].天津:天津大学,2008. PAN Lianrong.Research on the transformer fault diagnosis based on synergetic theory[D].Tianjin:Tianjin University,2008.(in Chinese)