改进的离散小波-优化极限学习机在倾转旋翼机故障诊断中的应用

doi:10.3969/j.issn.1000-1093.2014.11.025

兵工学报 ›› 2014, Vol. 35 ›› Issue (11): 1914-1921.doi: 10.3969/j.issn.1000-1093.2014.11.025

改进的离散小波-优化极限学习机在倾转旋翼机故障诊断中的应用

严峰¹，陈晓²，王新民²，彭程²，胡亚洲²

（1.中航工业直升机设计研究所，江西景德镇 330001， 2.西北工业大学自动化学院，陕西西安 710129)

收稿日期:2013-11-24 修回日期:2013-11-24 上线日期:2015-01-05
作者简介:严峰（1978—），男，高级工程师

Fault Diagnosis of Tiltrotor Aircraft via Improved Discrete Wavelet-OMELM

YAN Feng¹, CHEN Xiao², WANG Xin-min², PENG Cheng², HU Ya-zhou²

(1.AVIC China Helicopter Research and Development Institute, Jingdezhen 330001, Jiangxi, China;2.School of Automation, Northwestern Polytechnical University, Xi’an 710129, Shaanxi, China)

Received:2013-11-24 Revised:2013-11-24 Online:2015-01-05

摘要/Abstract

摘要： 针对倾转旋翼机飞控系统的故障诊断问题，提出一种改进的离散小波-优化极限学习机（OMELM）的故障诊断算法。提出自适应启发式小波去噪方法对采集的信号进行消噪，定义了帕塞瓦尔能量用来提取测量信号经离散小波变换分解后的特征，并对OMELM进行了改进。将提取的故障能量特征进行归一化后输入到改进的OMELM多分类器中进行分类，以美国XV-15倾转旋翼机为例进行仿真验证。结果表明文中方法平均辨识率高，诊断时间短，对未来我国进行倾转旋翼机故障诊断的研究有一定参考价值。

关键词: 航空、航天系统工程, 倾转旋翼机, 故障诊断, 离散小波, 优化极限学习机, 自适应启发式小波去噪

Abstract: An improved discrete wavelet-optimization method-based extreme learning machine (OMELM) algorithm is presented for the fault diagnosis of flight control system in tiltrotor aircraft. An adaptive heuristic wavelet denoising method is used to denoise the sampled signal. Feature vector of each layer is extracted using Parseval energy after the discrete wavelet decomposition of fault signal. The energy feature is normalized as the improved OMELM network input, and then the actuator fault models is classified using the improved OMELM network. Finally, an XV-15 tiltrotor aircraft mode is validated by simulation. The results show that the method has a higher average recognition rate, and needs a short diagnosis time.

Key words: aerospace system engineering, tiltrotor aircraft, fault diagnosis, discrete wavelet transform, optimization method-based extreme learning machine, adaptive heuristic wavelet denoising

中图分类号:

V275⁺.1

严峰，陈晓，王新民，彭程，胡亚洲. 改进的离散小波-优化极限学习机在倾转旋翼机故障诊断中的应用[J]. 兵工学报, 2014, 35(11): 1914-1921.

YAN Feng, CHEN Xiao, WANG Xin-min, PENG Cheng, HU Ya-zhou. Fault Diagnosis of Tiltrotor Aircraft via Improved Discrete Wavelet-OMELM[J]. Acta Armamentarii, 2014, 35(11): 1914-1921.

参考文献

［1］ Ferguson S W. A mathematical model for real time flight simulation of a generic tiltrotor aircraft, NASA CR-166536[R]. US: NASA,1988.
[2] Kleinhesselink K M. Stability and control modeling of tiltrotor aircraft[D].Maryland: University of Maryland, 2007.
[3] 宋彦国,王焕瑾，沙虹伟,等. 倾转旋翼飞行器飞行力学模型研究[J].空气动力学报,2008,26(2)：192-196
SONG Yan-guo, WANG Huan-jin, SHA Hong-wei， et al. Flight dynamic mathematical model of tiltrotor aircraft [J]. Acta Aerodynamica Sinca, 2008, 26(2):192-196 .（in Chinese)
[4] 周文雅，李立涛，杨涤.倾转旋翼航空器建模方法研究[J].飞行力学,2008,26(3)：5-9.
ZHOU Wen-ya, LI Li-tao, YANG Di.Study on tilt rotor aircraft modeling method [J]. Flight Dynamics, 2008,26(3):5-9. (in Chinese)
[5] Rysdyk R T, Calise A J. Adaptive model inversion flight control for tilt-rotor aircraft[J]. Journal of Guidance, Control, and Dynamics, 1999, 22(3): 402-407.
[6] Hathaway E L. Active and passive techniques for tilt rotor[D]. Pennsylvania: the Pennsylvania State University, 2010.
[7] 沙虹伟. 无人倾转旋翼机飞行力学建模与姿态控制技术研究[D].南京. 南京航空航天大学, 2007.
SHA Hong-wei.A mathematical model of unmanned tiltrotor aircraft and research on attitude control system design[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2007. (in Chinese)
[8] Park S, Bae J, Kim Y. Fault tolerant flight control system for the tilt-rotor UAV[J]. Journal of the Franklin Institute,2013,350(9): 2535-2559.
[9] Chow M Y, Mangum P, Thomas R J. Incipient fault detection in DC machines using a neural network[C]∥Proceedings of the 22nd Asilomar Conference on Signals, Systems, and Computers. San Jose, US: IEEE, 1988:706-709.
[10] 李晗,萧德云. 基于数据驱动的故障诊断方法综述[J].控制与决策,2011,26(1): 1-9.
LI Han, XIAO De-yun. Survey on data driven fault diagnosis methods[J]. Control and Decision,2011, 26(1):1-9. (in Chinese)
[11] 谢勇. 无人直升机飞行传感器仿真及其故障诊断与容错技术研究[D]. 南京: 南京航空航天大学, 2010.
XIE Yong.Research on servo control system of an airborne tracking and stabilization platform[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2010. (in Chinese)
[12] Marcello Napolitano, Younghwan An, Brad Seanor, et al. Application of a neural sensor validation scheme to actual Boeing B737 flight data[C]∥Guidance, Navigation and Control Conference and Exhibit. Portland, OR: AIAA, 1999：1531-1541.
[13] Brimker J S, Wise K. Flight testing of a reconfigurable control of a tailless fighter aircraft, AIAA-2000-3941 [R]. US: AIAA, 2000.
[14] 尹刚,张英堂,李志宁，等.基于在线半监督学习的故障诊断方法研究[J]. 振动工程学报,2012,25(6):637-642.
YIN Gang, ZHANG Ying-tang, LI Zhi-ning, et al.Fault diagnosis method based on online semi-supervised learning[J]. Journal of Vibration Engineering,2012, 25(6):637-642. (in Chinese)
[15] Huang G B, Zhu Q Y, Siew C K . Extreme learning machine: theory and applications[J]. Neurocomputing,2006,70(1): 489- 501.
[16] Liang N Y, Huang G B, Saratchandran P, et al. A fast and accurate online sequential learning algorithm for feedforward networks[J]. IEEE Transactions on Neural Networks, 2006,17(6): 411-1423.
[17] Huang G B, Chen I. Convex incremental extreme learning machine[J]. Neurocomputing,2007,70(16/17/18): 3056-3062.
[18] Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: a new learning scheme of feedforward neural networks[C]∥2004 IEEE International Joint Conference on Neual Networks,Vol 2. Budapest, Hungary: IEEE, 2004: 985-990.
[19] Lang F N, Yuan X, Zhou J L. Redundancy analysis of wavelet transform coefficient[J]. Acta Automatica Sinica, 2009, 33(4): 568-577.

[1]	王伽豪，那文波，刘志威，昝琪，王铮. 双闭环比值控制系统传感器故障诊断方法[J]. 兵工学报, 2024, 45(4): 1252-1263.
[2]	吕卫民, 孙晨峰, 任立坤, 赵杰, 李永强. 一种基于TCN-LGBM的航空发动机气路故障诊断方法[J]. 兵工学报, 2024, 45(1): 253-263.
[3]	魏剑峰, 张发平, 卢继平, 杨向飞, 杨鹏楷. 基于高斯模型和RMSD-DS的火炮反后坐装置故障诊断方法[J]. 兵工学报, 2023, 44(10): 3101-3114.
[4]	黄文宽, 钱林方, 尹强, 刘太素. 基于迁移学习的供药装置故障诊断方法[J]. 兵工学报, 2023, 44(10): 2964-2974.
[5]	李良钰，苏铁熊，马富康，吴小军，徐春龙. 基于集合经验模态分解-支持向量机的高压共轨系统故障诊断方法[J]. 兵工学报, 2022, 43(5): 992-1001.
[6]	李泽东，李志农，陶俊勇，毛清华，张旭辉. 基于特征融合的注意力增强卷积神经网络的航空发动机滚动轴承故障诊断方法[J]. 兵工学报, 2022, 43(12): 3228-3239.
[7]	李炜，韩寅龙，孙晓静. 基于特征优选与深度学习的车载电源微小故障诊断方法[J]. 兵工学报, 2022, 43(11): 2935-2944.
[8]	李志农, 胡志峰, 毛清华, 张旭辉, 陶俊勇. 非线性调频模态分解-同步提取变换方法及其在滚动轴承故障诊断中的应用[J]. 兵工学报, 2021, 42(6): 1324-1330.
[9]	朱敏，许爱强，许晴，李睿峰. 基于改进多层核超限学习机的模拟电路故障诊断[J]. 兵工学报, 2021, 42(2): 356-369.
[10]	李佳蔚，崔涛，刘宇航，师帅楠，孙强. 基于排气温度动态模型的在线观测器研究[J]. 兵工学报, 2019, 40(8): 1562-1571.
[11]	郭伟超，赵怀山，李成，李言，汤奥斐. 基于小波包能量谱与主成分分析的轴承故障特征增强诊断方法[J]. 兵工学报, 2019, 40(11): 2370-2377.
[12]	翟禹尧，史贤俊，吕佳朋. 基于广义随机Petri网的导弹系统测试性建模与指标评估方法研究[J]. 兵工学报, 2019, 40(10): 2070-2079.
[13]	张伟，刘星，许爱强，平殿发. l_p范数约束的模拟电路3层多核故障诊断模型[J]. 兵工学报, 2018, 39(7): 1352-1363.
[14]	任海锋，潘宏侠. 基于多分形特征的枪械自动机裂纹故障诊断[J]. 兵工学报, 2018, 39(3): 457-462.
[15]	王英敏，崔涛，张付军，董天普. 基于局部线性模型树模型的柴油机进气系统漏气和堵塞故障诊断[J]. 兵工学报, 2017, 38(8): 1457-1468.

改进的离散小波-优化极限学习机在倾转旋翼机故障诊断中的应用

Fault Diagnosis of Tiltrotor Aircraft via Improved Discrete Wavelet-OMELM

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价