Ahmad Muhammad Waqas, Akram Muhammad Usman, Ahmad Rashid, Hameed Khurram, Hassan Ali
Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad, 44000, Pakistan.
ISA Trans. 2022 Oct;129(Pt A):355-371. doi: 10.1016/j.isatra.2022.01.014. Epub 2022 Jan 20.
Autonomous flights are the major industry contributors towards next-generation developments in pervasive and ubiquitous computing. Modern aerial vehicles are designed to receive actuator commands from the primary autopilot software as input to regulate their servos for adjusting control surfaces. Due to real-time interaction with the actual physical environment, there exists a high risk of control surface failures for engine, rudder, elevators, and ailerons etc. If not anticipated and then timely controlled, failures occurring during the flight can have severe and cataclysmic consequences, which may result in mid-air collision or ultimate crash. Humongous amount of sensory data being generated throughout mission-critical flights, makes it an ideal candidate for applying advanced data-driven machine learning techniques to identify intelligent insights related to failures for instant recovery from emergencies. In this paper, we present a novel framework based on machine learning techniques for failure prediction, detection, and classification for autonomous aerial vehicles. The proposed framework utilizes long short-term memory recurrent neural network architecture to analyze time series data and has been applied at the AirLab Failure and Anomaly flight dataset, which is a comprehensive publicly available dataset of various fault types in fixed-wing autonomous aerial vehicles' control surfaces. The proposed framework is able to predict failure with an average accuracy of 93% and the average time-to-predict a failure is 19 s before the actual occurrence of the failure, which is 10 s better than current state-of-the-art. Failure detection accuracy is 100% and average detection time is 0.74 s after happening of failure, which is 1.28 s better than current state-of-the-art. Failure classification accuracy of proposed framework is 100%. The performance analysis shows the strength of the proposed methodology to be used as a real-time failure prediction and a pseudo-real-time failure detection along with a failure classification framework for eventual deployment with actual mission-critical autonomous flights.
自主飞行是推动普适计算和泛在计算下一代发展的主要行业力量。现代飞行器被设计为接收来自主自动驾驶软件的执行器命令作为输入，以调节其舵机来调整控制面。由于与实际物理环境的实时交互，发动机、方向舵、升降舵和副翼等控制面存在很高的故障风险。如果未能提前预测并及时控制，飞行过程中发生的故障可能会导致严重的灾难性后果，可能会导致空中碰撞或最终坠毁。在关键任务飞行过程中会产生大量的传感数据，这使其成为应用先进的数据驱动机器学习技术来识别与故障相关的智能见解以实现紧急情况即时恢复的理想候选对象。在本文中，我们提出了一种基于机器学习技术的新颖框架，用于自主飞行器的故障预测、检测和分类。所提出的框架利用长短期记忆循环神经网络架构来分析时间序列数据，并已应用于AirLab故障与异常飞行数据集，该数据集是固定翼自主飞行器控制面各种故障类型的全面公开可用数据集。所提出的框架能够以93%的平均准确率预测故障，在故障实际发生前平均预测故障的时间为19秒，比当前的最先进技术快10秒。故障检测准确率为100%，故障发生后平均检测时间为0.74秒，比当前的最先进技术快1.28秒。所提出框架的故障分类准确率为100%。性能分析表明，所提出的方法可作为实时故障预测、准实时故障检测以及故障分类框架，最终用于实际关键任务自主飞行。
