An audio‐based risky flight detection framework for quadrotors
An audio‐based risky flight detection framework for quadrotors
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DOI：
10.1049/csy2.12105
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发表时间：
2024-01
期刊：
影响因子：
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通讯作者：
Wansong Liu;Chang Liu;S. Sajedi;Hao Su;Xiao Liang;Minghui Zheng
中科院分区：
文献类型：
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作者：
Wansong Liu;Chang Liu;S. Sajedi;Hao Su;Xiao Liang;Minghui Zheng
无人机越来越多地在一些工作场所与人类工人合作，比如仓库。在一些空中任务中，无人机飞行失败可能会给人类的生命安全带来潜在的风险。最常见的飞行故障之一是由损坏的螺旋桨引发的。为了快速检测螺旋桨的物理损伤，识别危险飞行，并向周围的人类工作人员提供早期警告，提出了一种新的全面的故障诊断框架，该框架只使用螺旋桨旋转引起的音频，而不访问任何飞行数据。该诊断框架包括三个组成部分：利用卷积神经网络、转移学习和贝叶斯优化。具体地说，来自实际飞行的音频信号被采集并传输到时频频谱图中。首先，开发了一个基于卷积神经网络的诊断模型，该模型利用这些光谱图来识别在特定的无人机飞行中是否有任何损坏的螺旋桨。此外，作者还使用蒙特卡罗辍学抽样来获得诊断结果的不一致性，并计算平均概率得分向量的熵(不确定性)作为诊断无人机飞行的另一个因素。其次，为了减少数据对不同无人机类型的依赖，通过传递学习进一步扩展了基于卷积神经网络的诊断模型。也就是说，通过使用来自不同无人机的一小组数据来提炼训练有素的诊断模型的知识。改进后的诊断模型具有检测第二架无人机螺旋桨断裂的能力。第三，为了减少超参数的调整工作量，增强网络的稳健性，贝叶斯优化算法利用观测到的诊断模型性能构造了一个高斯过程模型，允许采集函数选择最优的网络超参数。通过实际试飞验证，该诊断框架具有较高的诊断准确率。
Drones have increasingly collaborated with human workers in some workspaces, such as warehouses. The failure of a drone flight may bring potential risks to human beings' life safety during some aerial tasks. One of the most common flight failures is triggered by damaged propellers. To quickly detect physical damage to propellers, recognise risky flights, and provide early warnings to surrounding human workers, a new and comprehensive fault diagnosis framework is presented that uses only the audio caused by propeller rotation without accessing any flight data. The diagnosis framework includes three components: leverage convolutional neural networks, transfer learning, and Bayesian optimisation. Particularly, the audio signal from an actual flight is collected and transferred into time–frequency spectrograms. First, a convolutional neural network‐based diagnosis model that utilises these spectrograms is developed to identify whether there is any broken propeller involved in a specific drone flight. Additionally, the authors employ Monte Carlo dropout sampling to obtain the inconsistency of diagnostic results and compute the mean probability score vector's entropy (uncertainty) as another factor to diagnose the drone flight. Next, to reduce data dependence on different drone types, the convolutional neural network‐based diagnosis model is further augmented by transfer learning. That is, the knowledge of a well‐trained diagnosis model is refined by using a small set of data from a different drone. The modified diagnosis model has the ability to detect the broken propeller of the second drone. Thirdly, to reduce the hyperparameters' tuning efforts and reinforce the robustness of the network, Bayesian optimisation takes advantage of the observed diagnosis model performances to construct a Gaussian process model that allows the acquisition function to choose the optimal network hyperparameters. The proposed diagnosis framework is validated via real experimental flight tests and has a reasonably high diagnosis accuracy.