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摘要: 无人机的飞控系统故障诊断主要面临以下两大挑战：其一，作为新兴的空中飞行平台，无人机可用于故障诊断的有效训练数据规模有限，存在显著的训练数据匮乏问题；其二，作为高机动性空中飞行平台，无人机在不同飞行姿态下的数据分布差异显著，存在数据环境高度变动的问题。针对这两种挑战，该文提出了一种结合姿态不变性特征和半监督RDC-GAN模型的飞控系统故障诊断方法。方法首先通过基于微分平坦的数据筛选将无人机数据分为姿态相关数据和姿态不相关数据；对于姿态相关数据，利用EMD-SENet提取对姿态变化具有鲁棒性的姿态不变性特征；之后采用自适应特征融合模块将姿态不相关数据、姿态相关数据和提取到的姿态不变性特征进行加权融合；最后将融合特征送入半监督RDC-GAN模型进行两阶段训练：第一阶段采用无监督训练，利用大量无标签数据对模型网络权重初始化，第二阶段采用有监督训练，通过少量有标签数据进一步对网络权重进行微调，从而实现仅用少量有效数据就能精确诊断出无人机飞控故障的目的。方法在公开数据集RflyMad上整体精度达到了95.71%，在真机故障诊断实验中的整体精度达到了92.78%。
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表 1 改进的RDNet网络结构
层级名称 所包含主要结构 描述 输出特征图尺寸 输入层 / 输入数据 128$ × $128 头卷积层 [4$ × $4 conv]+LayerNorm 改变特征尺寸和通道数 32$ × $32 Staget1 $ \left[\begin{matrix}7×7\mathrm{conv}\\ 1×{1}_{}\mathrm{conv}\\ \end{matrix}\right]×4 $
[1$ × $1 conv] $ × $44个Dense Block
4个Stage内过渡层
增长率6432$ × $32 过渡层 [2$ × $2 conv]+LayerNorm Stage间过渡层 16$ × $16 Staget2 $ \left[\begin{matrix}7×7\mathrm{conv}\\ 1×{1}_{}\mathrm{conv}\\ \end{matrix}\right]×4 $
[1$ × $1 conv] $ × $44个Dense Block
4个Stage内过渡层
增长率10416$ × $16 过渡层 [2$ × $2 conv]+LayerNorm Stage间过渡层 8$ × $8 Staget3 $ \left\{\left[\begin{matrix}7×7\mathrm{conv}\\ 1×{1}_{}\mathrm{conv}\\ \end{matrix}\right]×4\right\}×4 $
{[1$ × $1 conv] $ × $4}$ × $44组Dense Block
增长率为128
4组Stage内过渡层8$ × $8 输出层 全局池化+LayerNorm
[3$ × $3 conv]$ × $3
全连接层输出结果 1$ × $1 
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表 2 姿态不变性特征(Zsa)和姿态相关数据(Zre)Fisher Score对比
数据类型 统计特征法 向量距离法 Zsa Zre Zsa Zre 磁力计x 125.89 0.28 15.61 2.10 磁力计y 104.16 0.02 12.46 1.08 磁力计z 465.91 4.55 35.37 11.01 陀螺仪x 192.04 28.50 6.40 2.58 陀螺仪y 129.78 9.37 3.47 0.79 陀螺仪z 383.27 8.23 9.76 3.55 加速度计x 4.62 0.17 6.69 2.77 加速度计y 8.66 0.12 2.01 0.55 加速度计z 1.53 0.24 0.66 0.26 均值 186.42 8.54 13.45 2.37
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表 3 各类方法在RflyMad 数据集上诊断精度 单位：%
方法 方法[12] 方法[13] 方法[14] 方法[8] 方法[15] 方法[16] 方法[17] 本文方法 GNSS故障 85.31±1.25 92.25±1.51 92.41±1.21 91.21±1.31 90.12±1.42 92.45±0.87 93.21±0.75 95.29±0.58 加速度计故障 87.13±1.52 90.34±1.04 90.27±1.64 90.07±0.93 88.26±1.13 91.63±0.79 91.97±0.77 93.54±0.49 磁力计故障 83.28±0.56 88.92±0.86 89.34±0.87 86.32±0.62 87.18±0.56 91.12±0.64 91.25±0.65 93.51±0.37 陀螺仪故障 85.45±0.43 89.35±1.56 90.15±0.55 88.41±0.97 85.91±0.52 91.87±0.61 92.27±0.48 94.27±0.41 无故障 94.16±0.28 97.39±0.43 97.83±0.29 97.62±0.34 95.56±0.44 97.51±0.32 97.43±0.24 100±0.00 整体精度 88.41±0.86 91.89±0.82 92.15±0.74 91.57±0.76 90.08±0.62 92.84±0.61 93.54±0.51 95.71±0.25 平均精度 87.06±1.07 91.65±1.12 92.04±0.81 90.72±0.59 89.41±0.53 92.53±0.54 92.90±0.45 95.32±0.31 卡帕系数 87.91±0.94 91.56±1.04 92.07±0.78 91.25±0.62 89.52±0.65 92.64±0.59 93.01±0.46 95.41±0.29
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表 4 主干网络模型对比
主干网络 参数量/M FLOPs/G 推理耗时/ms 整体精度/% 平均精度/% 卡帕系数/% RDNet 12.85 1.15 2.94±0.037 95.71±0.25 95.32±0.31 95.41±0.29 ResNet-50 23.51 1.32 1.73±0.024 93.43±0.67 93.25±0.89 93.37±0.73 DenseNet-121 6.96 0.92 4.43±0.051 93.97±0.53 93.51±0.65 93.53±0.68 ConvNeXt-tiny 28.58 1.46 1.35±0.048 94.24±0.49 93.91±0.54 94.08±0.51
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表 5 实机飞行故障诊断时间统计表 单位：s
飞行状态 匀速模式 盘旋模式 悬停模式 磁力计故障 1.02±0.13 0.94±0.11 1.35±0.11 加速度计故障 1.25±0.14 1.05±0.09 1.41±0.08 陀螺仪故障 0.93±0.08 0.89±0.07 1.15±0.09 GNSS故障 0.62±0.07 0.63±0.08 0.73±0.06
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表 6 实机飞行故障诊断虚警统计表
匀速模式 盘旋模式 悬停模式 磁力计故障 2次 1次 3次 加速度计故障 2次 0次 1次 陀螺仪故障 1次 1次 2次 GNSS故障 0次 0次 0次 总持续时间 4.94s 1.33s 6.31s
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