Acta Aeronautica et Astronautica Sinica ›› 2023, Vol. 44 ›› Issue (S1): 727615-727615.doi: 10.7527/S1000-6893.2022.27615
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Zhongzhi LI, Jinyi MA, Jianliang AI, Yiqun DONG()
Received:
2022-06-01
Revised:
2022-06-15
Accepted:
2022-06-22
Online:
2023-06-25
Published:
2022-06-24
Contact:
Yiqun DONG
E-mail:yiqundong@fudan.edu.cn
Supported by:
CLC Number:
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.
Table 1
Overview of aircraft fault flight database used in this paper[34]
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] | [ | 151 |
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 |
Fig.1
Schematic diagram of sketch data stacking [26] (taking three groups of sensors of velocity, angle of attack and sideslip angle as examples, left: intercept data in time window and downsampling; middle and right: data stacking and normalization; for a total of 15 groups of sensor data, the data is intercepted in a 30 s time window and down sampled at 1 Hz, then 15 × 31 pixel dimension sensor measurement data gray image)
Fig.4
Schematic diagram of proposed VGG16 aeronautical sensor fault detection and classification network structure (after being processed by mosaic diagram, sensor fault is visually reflected as abnormal area on mosaic diagram; connect mosaic diagram to VGG16 network and define number of network output categories as 10 (number of fault categories), and directly use fine-tuning method to train and test network)
Table 3
Fault detection and classification network 5-fold cross-validation training and test accuracy
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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