Park Kyung Ho, Park Eunji, Kim Huy Kang
Graduate School of Cybersecurity, Korea University, Seoul 02841, Korea.
Sensors (Basel). 2021 Mar 22;21(6):2208. doi: 10.3390/s21062208.
Unmanned Aerial Vehicles are expected to create enormous benefits to society, but there are safety concerns in recognizing faults at the vehicle's control component. Prior studies proposed various fault detection approaches leveraging heuristics-based rules and supervised learning-based models, but there were several drawbacks. The rule-based approaches required an engineer to update the rules on every type of fault, and the supervised learning-based approaches necessitated the acquisition of a finely-labeled training dataset. Moreover, both prior approaches commonly include a limit that the detection model can identify the trained type of faults only, but fail to recognize the unseen type of faults. In pursuit of resolving the aforementioned drawbacks, we proposed a fault detection model utilizing a stacked autoencoder that lies under unsupervised learning. The autoencoder was trained with data from safe UAV states, and its reconstruction loss was examined to distinguish the safe states and faulty states. The key contributions of our study are, as follows. First, we presented a series of analyses to extract essential features from raw UAV flight logs. Second, we designed a fault detection model consisting of the stacked autoencoder and the classifier. Lastly, we validated our approach's fault detection performance with two datasets consisting of different types of UAV faults.
无人驾驶飞行器有望为社会带来巨大益处，但在识别飞行器控制组件的故障方面存在安全隐患。先前的研究提出了各种利用基于启发式规则和基于监督学习的模型的故障检测方法，但存在一些缺点。基于规则的方法要求工程师针对每种故障类型更新规则，而基于监督学习的方法则需要获取经过精细标注的训练数据集。此外，这两种先前的方法通常都有一个局限性，即检测模型只能识别已训练过的故障类型，而无法识别未见过的故障类型。为了解决上述缺点，我们提出了一种利用堆叠自动编码器的故障检测模型，该模型属于无监督学习。自动编码器使用来自无人机安全状态的数据进行训练，并通过检查其重建损失来区分安全状态和故障状态。我们研究的主要贡献如下。首先，我们进行了一系列分析，以从原始无人机飞行日志中提取基本特征。其次，我们设计了一个由堆叠自动编码器和分类器组成的故障检测模型。最后，我们使用由不同类型无人机故障组成的两个数据集验证了我们方法的故障检测性能。
