Intelligent framework for automated failure prediction, detection, and classification of mission critical autonomous flights

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.