基于TSFFCNN-PSO-SVM的飞机起落架液压系统故障诊断

冯东洋; 姜春英; 鲁墨武; 叶长龙; 李胜宇

doi:10.13224/j.cnki.jasp.20220111

基于TSFFCNN-PSO-SVM的飞机起落架液压系统故障诊断

doi: 10.13224/j.cnki.jasp.20220111

1.
沈阳航空航天大学机电工程学院，沈阳 110136
2.
沈阳航空航天大学计算机学院，沈阳 110136

基金项目: 辽宁省自然科学基金（2019-KF-01-11）

详细信息

作者简介:
冯东洋（1996-），男，硕士生，主要从事航空航天领域关于液压技术与故障诊断方面的工作

通讯作者:
姜春英（1978-），女，副教授、硕士生导师，博士，主要从事空天装备与测控技术研究工作。E-mail：99448588@qq.com

中图分类号: V37；TP227
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出版历程
- 收稿日期: 2022-03-04
- 网络出版日期: 2023-03-21

Fault diagnosis of aircraft landing gear hydraulic system based on TSFFCNN-PSO-SVM

1.
School of Mechatronics Engineering，Shenyang Aerospace University，Shenyang 110136，China
2.
School of Computer Science，Shenyang Aerospace University，Shenyang 110136，China

摘要

摘要:
针对飞机起落架液压系统故障诊断精度低，深层故障特征提取困难的问题，提出了一种基于双路特征融合卷积神经网络（TSFFCNN）与粒子群优化支持向量机（PSO-SVM）结合的起落架液压系统故障诊断模型。该诊断模型以起落架多节点压力信号作为输入，采用一维卷积神经网络（1DCNN）与二维卷积神经网络（2DCNN）并行多通道网络结构自适应提取深层特征信息，并在融合层将深层特征信息融合，通过优化后的SVM分类器对融合特征进行故障分类。为验证所提诊断模型的故障分类效果，基于AMESim搭建了典型飞机起落架液压系统仿真模型，构建了几种典型故障类型数据集。基于仿真数据的诊断结果表明，所提故障诊断算法精度能达到99.37%，能够有效实现起落架液压系统故障诊断；与其他智能算法对比，基于TSFFCNN-PSO-SVM故障诊断模型具有更好的平稳性与可靠性，诊断精度更高。
- 飞机起落架液压系统 /
- 特征融合 /
- 故障诊断 /
- 双路特征融合卷积神经网络（TSFFCNN） /
- 粒子群优化支持向量机（PSO-SVM）
Abstract:
In view of the problems of low fault diagnosis accuracy of aircraft landing gear hydraulic system and difficulty in extracting deep fault features, a fault diagnosis model of landing gear hydraulic system based on the combination of two-stream feature fusion convolutional neural network (TSFFCNN) and particle swarm optimization support vector machine (PSO-SVM) was proposed. The diagnosis model took the pressure signal of multiple nodes as input, the 1D convolutional neural network (1DCNN) and 2D convolutional neural network (2DCNN) parallel multi-channel network structures were adopted to adaptively extract deep feature information, and the deep feature information was fused in the fusion layer. The fusion features were classified into faults through the optimized SVM classifier. In order to verify the proposed fault diagnosis model, a typical aircraft landing gear hydraulic system simulation model was built based on AMESim, and several typical fault type data sets were constructed. The diagnostic results based on the simulation data showed that the accuracy of the proposed fault diagnosis algorithm can reach 99.37%, which can effectively realize the fault diagnosis of the landing gear hydraulic system; compared with other intelligent algorithms, the fault diagnosis model based on TSFFCNN-PSO-SVM had better stability and reliability, higher diagnosis accuracy.

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图 1 基于TSFFCNN-PSO-SVM故障诊断模型结构

Figure 1. Structure of fault diagnosis model based on TSFFCNN-PSO-SVM

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图 2 基于TSFFCNN-PSO-SVM的故障诊断算法流程

Figure 2. Fault diagnosis algorithm flow based on TSFFCNN-PSO-SVM

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图 3 起落架液压系统原理图

Figure 3. Schematic diagram of hydraulic system of landing gear

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图 4 AMESim仿真模型

Figure 4. Simulation model based on AMESim

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图 5 各节点压力信号变化

Figure 5. Change of pressure signal at node

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图 6 样本重叠分割

Figure 6. Sample overlap segmentation

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图 7 数据重构

Figure 7. Singal resheep

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图 8 各层特征的 t-SNE 可视化分析

Figure 8. t-SNE visual analysis of features at various levels

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图 9 CNN-SVM故障诊断过程

Figure 9. CNN-SVM fault diagnosis process

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图 10 故障诊断模型的混淆矩阵

Figure 10. Confusion matrix of fault diagnosis model

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图 11 加噪测试集的准确度

Figure 11. Accuracy on the noisy test set

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图 12 不同算法的故障分类效果

Figure 12. Failure classification effect of different methods

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表 1 TSFCNN层参数

Table 1. TSFCNN layer parameters

网络层	尺寸	通道数	Filter大小	步长
输入层-1D	1×1 024	3
卷积层1	1×1 024	24	1×3	1
池化层1	1×512	24	1×2	1
卷积层2	1×512	48	1×3	1
池化层2	1×256	48	1×2	1
Flatten层	1×4 096	3
输入层-2D	32×32	3
卷积层1	32×32	24	3×3	1
池化层1	16×16	24	2×2	1
卷积层2	16×16	48	3×3	1
池化层2	8×8	48	2×2	1
Flatten层	1×1 024	3
特征融合层	1×5 120	3
全连接层	1×512	3
PSO-SVM	1×5	1

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表 2 PSO算法超参数设置

Table 2. PSO algorithm hyperparameter settings

$\omega $	${c_1}$	${c_2}$	${r_1}$	${r_2}$
0.5	0.9	0.9	0.1	0.1

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表 3 AMESim模型中元件含义

Table 3. Meaning of components in AMESim model

编号	元件	备注
1	液压油箱	油箱内压力：0.34 MPa
2	液压泵	转速：5 000 r/min
3	液压泵泄漏	孔径：<1 mm（正常），>1 mm（泄漏）
4	供油油滤	等效油滤孔径：5～7 mm
5	单向阀	开启压力：0.5 MPa
6	蓄能器	预充压力：13 MPa，容积：2.62 L
7	油路选择阀	三位四通换向阀
8	锁作动筒	推杆长度：0.2 m
9	收放作动筒	推杆长度=0.014 m
10	选择阀堵塞	等效孔径：5～7 mm（正常）， <4 mm（堵塞）
11	作动筒泄漏	等效孔径：<1 mm（正常）， >1 mm（泄漏）
12	溢流阀	开启压力：15 MPa

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