拟VGG16网络的航空传感器故障检测分类

doi:10.7527/S1000-6893.2022.27615

Abstract

Abstract:

Following the research advances in machine vision， a concept defined as imagification-based intelligence has been proposed. A fault detection and classification method of fault detection and classification was developed using a deep neural network finetuned from VGG16， which originally was designed for image detection and classification problems. Firstly， a database corresponding to 4 aerospace vehicles and 5 flight conditions were constructed via both flight simulations and real-vehicle tests. Faults of aerospace sensors and inertial measurement unit are included in the database. Secondly， we proposed to stack the measurement data of aerospace sensors into a grayscale image， which was used to transfer the fault detection and classification problem into abnormal regions detection problem on the image. Thirdly， the size of the grayscale image was augmented to match the input size of VGG16， and the fault detection neural network was developed via finetuning the VGG16 directly. The experimental results on several aircraft show that the average test accuracy of the network reaches 97.6%. Following the explainability analysis methods in machine vision， Class Activation Mapping （CAM） results of different layers of the fault detection neural network were plotted， which solidifies the performance of the proposed neural network.

Key words: aerospace sensors, fault detection and classification, imagification-based intelligence, deep neural networks, explainability analysis

CLC Number:

V267

Zhongzhi LI, Jinyi MA, Jianliang AI, Yiqun DONG. Fault detection and classification of aerospace sensors using deep neural networks finetuned from VGG16[J]. Acta Aeronautica et Astronautica Sinica, 2023, 44(S1): 727615-727615.

Figures/Tables 9

Table 1

Table 2

Definition of fault form and amplitude of different sensor faults［9-11］

故障标签	对应传感器信号	故障注入形式	故障注入幅值
9	${G x, G y, G z}$	附加噪声	0.2g
8	${w x, w y, w z}$	附加噪声	10 （°）/s
7	${G x, G y, G z}$	测量值漂移	±（0.1~0.3）g
6	${w x, w y, w z}$	测量值漂移	±（10~15）（°）/s
5	$β$	附加噪声	5°
4	$α$	附加噪声	5°
3	$β$	测量值漂移	±（5°~10°）
2	$α$	测量值漂移	±（5°~10°）
1	$V$	测量值漂移	-（0.5~1） m/s
0	传感器均无故障

Table 2

Fig.1

Fig.2

Fig.3

Fig.4

Table 3

Fig.5

Fig.6

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航空器	构型概况	质量/t	翼展/m	飞行状态及数据来源	速度区间/（m·s^-1）	迎角区间/（°）	数据时长/min
Y	军用运输机，四发	41.0	38	低空起降，飞行员操纵，飞行仿真	［93， 167］	［-2.7， 7.7］	295
B1	民航客机，四发	174.0	60	近地飞行，飞行员操纵，飞行仿真	［47， 276］	［14， 19］	151
B1	民航客机，四发	174.0	60	高空巡航，驾驶仪操纵，飞行仿真	［227， 252］	［-1.3， 0.6］	327
B2	民航客机，四发	44.6	36	低空起降，飞行员操纵，实机飞行	［75， 151］	［0.8， 6.7］	67
D	通航飞机，双发	3.1	20	高空巡航，驾驶仪操纵，飞行仿真	［68， 71］	［0.5， 3.2］	162

训练详情	准确度	准确率（宏平均）	F₁值（宏平均）	召回率（宏平均）
第1次训练	0.99	0.99	1.00	0.99
第2次训练	0.96	0.96	0.96	0.96
第3次训练	0.98	0.97	0.99	0.98
第4次训练	0.96	0.96	0.98	0.97
第5次训练	0.99	0.98	0.99	0.98
均值	0.976	0.972	0.984	0.976
标准差	0.013 565	0.011 662	0.013 565	0.010 198

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Fault detection and classification of aerospace sensors using deep neural networks finetuned from VGG16

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