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论文:2023,Vol:41,Issue(1):230-240 |
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引用本文： |
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李旭东, 李艳军, 曹愈远, 王兴业, 段仕轩, 赵泽剑. 基于CNN-SVM的飞机EHA故障诊断算法研究[J]. 西北工业大学学报 |
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LI Xudong, LI Yanjun, CAO Yuyuan, WANG Xingye, DUAN Shixuan, ZHAO Zejian. Study on fault diagnosis algorithms of EHA based on CNN-SVM[J]. Journal of Northwestern Polytechnical University |
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| 基于CNN-SVM的飞机EHA故障诊断算法研究 |
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| 李旭东, 李艳军, 曹愈远, 王兴业, 段仕轩, 赵泽剑 |
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| 南京航空航天大学 民航学院, 江苏 南京 211106 |
| 摘要: |
| 针对飞机电动静液作动器(electro-hydrostatic actuator,EHA)系统集成度高、工况复杂、故障种类多的特点,为了对其典型故障进行有效诊断,提出一种基于卷积神经网络(convolutional neural networks,CNN)和支持向量机(support vector machine,SVM)的故障诊断算法。使用CNN对故障数据进行自适应特征提取,再利用SVM对CNN全连接层输出进行分类。为提高SVM分类性能,使用带动态惯性权重的自适应粒子群优化算法(dynamic inertia weight adaptive particle swarm optimization,IWAPSO)实现对SVM参数的优化选择。引入Ramp损失函数降低SVM的噪声敏感性。结果表明:经过参数优化后的SVM准确率比标准SVM提升了12.6%,比传统CNN方法提升了17.3%;当使用含噪声信号的测试集时,基于Ramp损失函数的SVM表现出了更好的鲁棒性。 |
| 关键词:
电动静液作动器
卷积神经网络
支持向量机
粒子群优化算法
Ramp损失函数
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| Study on fault diagnosis algorithms of EHA based on CNN-SVM |
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| LI Xudong, LI Yanjun, CAO Yuyuan, WANG Xingye, DUAN Shixuan, ZHAO Zejian |
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| College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China |
| Abstract: |
| Contrapose the highly integrated, complex working conditions and many kinds of faults of aircraft electro hydrostatic actuator(EHA), to diagnose the typical fault of EHA effectively, a fault diagnosis algorithm based on convolutional neural networks (CNN) and support vector machine(SVM) was proposed. Firstly, the fault date sets are entered on CNN for adaptive feature extraction, then the output of the fully connected layer of CNN are classified by using SVM. To improve the performance of SVM, dynamic inertia weight adaptive particle swarm optimization (IWAPSO) was used to optimize the SVM parameters. Finally, the sensitivity of SVM to noise was reduced by introducing ramp loss function. The results show that the accuracy of SVM after parameter optimization is 12.6% higher than that of standard SVM and 17.3% higher than CNN. The SVM based on the ramp loss function showed better robustness when using noisy test sets. |
| Key words:
electro-hydrostatic actuator
convolutional neural networks
support vector machine
particle swarm optimization
ramp loss function
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| 收稿日期: 2022-05-18
修回日期:
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| DOI: 10.1051/jnwpu/20234110230 |
| 基金项目: 航空科学基金(20200033052001)资助 |
| 通讯作者: 李艳军(1969-),南京航空航天大学教授,主要从事飞机健康检测、诊断维护研究。e-mail:lyj@nuaa.edu.cn
Email：lyj@nuaa.edu.cn |
| 作者简介: 李旭东(1999-),南京航空航天大学硕士研究生,主要从事飞机健康检测、诊断维护研究。
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参考文献: |
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[1] 欧阳小平. 现代飞机液压技术[M]. 杭州:浙江大学出版社, 2016:166-171 OUYANG Xiaoping. Modern hydraulics for aircrafts[M]. Hangzhou:Zhejiang University Press, 2016:166-171 (in Chinese)
[2] NAWAZ M H, YU L, LIU H. Analytical method for fault detection & isolation in electro-hydrostatic actuator using bond graph modeling[C]//14th International Bhurban Conference on Applied Sciences and Technology, New York, 2017:312-317
[3] DALLA VEDOVA M D L, BERRI P C, BONANNO G, et al. Fault detection and identification method based on genetic algorithms to monitor degradation of electrohydraulic servomechanisms[C]//4th International Conference on System Reliability and Safety, New York, 2019:304-311
[4] 陈换过, 刘培君, 俞杭. 基于改进PCA-SOM的电静压伺服作动器油滤堵塞故障诊断[J]. 中国机械工程, 2021, 32(7):799-805 CHEN Huanguo, LIU Peijun, YU Hang. Identification of fault degree of oil filter blockage in electro-hydraulic actuator on an improved PCA-SOM[J]. China Mechanical Engineering, 2021, 32(7):799-805 (in Chinese)
[5] 肖雪, 赵守军, 陈克勤, 等. 电静压伺服机构故障诊断中的主成分分析方法应用[J]. 导弹与航天运载技术, 2019, 1:94-100 XIAO Xue, ZHAO Shoujun, CHEN Keqin, et al. Application of principal component analysis in fault diagnosis of electro-hydrostatic actuators[J]. Missiles and Space Vehicles, 2019(1):94-100 (in Chinese)
[6] 赵杰彦, 胡健, 姚建勇, 等. 基于自适应神经网络鲁棒观测器的EHA故障诊断与容错控制[J]. 北京航空航天大学学报, 2022(1):1-16 ZHAO Jieyan, HU Jian, YAO Jianyong, et al. EHA fault diagnosis and fault tolerant control based on adaptive neural network robust observer[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022(1):1-16 (in Chinese)
[7] 金磊磊, 梁红, 杨长生. 基于卷积神经网络的水下目标声呐图像识别方法[J]. 西北工业大学学报, 2021, 39(2):285-291 JIN Leilei, LIANG Hong, YANG Changshen. Sonar image recognition of underwater target based on convolutional neural network[J]. Journal of Northwestern Polytechnical University, 2021, 39(2):285-291 (in Chinese)
[8] CORTES C, VAPNIK V. Support-vector networks[J]. Machine Learning, 1995, 20(3):273-297
[9] BURGES C J C, A tutorial on support vector machines for pattern recognition[J]. Data Mining and Knowledge Discovery, 1998, 2(2):121-167
[10] CRISTIANINI N, TAYLOR J S. An efficient intrusion to support vector machines and other kernel-based learning methods[M]. New York:Cambridge University Press, 2000:48-49
[11] 王喜宾. 基于优化支持向量机的个性化推荐研究[D]. 重庆:重庆大学, 2015 WANG Xibin. Research on personalized recommendation based on optimized support vector machine[D]. Chongqing:Chongqing University, 2015 (in Chinese)
[12] SHI Y H, EBERHART R. A modified particle swarm optimizer[C]//Proceedings of the IEEE World Congress on Computational Intelligence, 1998:69-73
[13] 滕志军, 吕金玲, 郭力文, 等. 一种基于Tent映射的混合灰狼优化的改进算法[J]. 哈尔滨工业大学学报, 2018, 50(11):40-49 TENG Zhijun, LYU Jinling, GUO Liwen, et al. An improved hybrid grey wolf optimization algorithm based on Tent mapping[J]. Journal of Harbin Institute of Technology, 2018, 50(11):40-49 (in Chinese)
[14] 刘大连. 稀疏非平行支持向量机与最优化[M]. 北京:北京邮电大学出版社, 2017:18-19 LIU Dalian. Sparse non-parallel support vector machines and optimization[M]. Beijing:Beijing University of Posts and Telecommunications Press, 2017:18-19 (in Chinese)
[15] YUILLE A L, RANGARAJAN A. The concave-conve-convex procedure[J]. Neural Computation, 2003, 15:915-936 |
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