Architecture and Algorithm Design for Civil Aviation Data Real-Time Analysis System

Abstract

Currently, the analysis of civil aviation flight data relies primarily on post-flight data. This involves transmitting onboard data to the servers of the airline and aircraft manufacturer after the aircraft has landed, and the engines have been shut down, using ground-based cellular stations or wireless hotspots. Real-time data analysis during flight remains a challenge. With the advent of integrated communication networks in the aerospace sector, the real-time transmission of onboard data is poised to become a future trend. But there is a gap in real-time civil aviation data analysis system. This study is based on Kappa distributed computing architecture, established a real-time aviation data analysis system, which inputs Quick Access Recorder (QAR) data stream and achieves fast data decoding and multiple real-time analysis algorithms, including flight trajectory outlier repair, flight phase identification, steady cruising state determination, and engine lubricating oil quantity monitoring. The results of simulation experiments indicate that the maximum average latency for individual algorithms is consistently below 250 milliseconds, meeting real-time performance requirements. And all real-time algorithms have similar performances with or better than existing excellent post-flight algorithms. Specifically, the flight trajectory outlier repair algorithm can repair all outliers and cost 0.2ms to process an outlier. The flight phase identification achieved 97.8% accuracy referring to commercial software’s results. The steady cruising state determination exhibits computational efficiency approximately five times faster than the naive method. The proposed engine lubricating oil quantity monitoring method based on the Clustering Optimized Transformer (CO-Transformer) neural network yields an average mean square error of approximately 0.057 L2 between predicted and actual values, respectively 7.36%, 66.01% and 66.05% better than common Transformer, LSTM and GRU neural network, satisfying the airborne leakage monitoring requirements for engine lubrication systems. The system also exhibits strong load capacity and excellent scalability. Under the employed hardware conditions, the system can concurrently process several practical real-time QAR data streams for up to 505 civilian planes in simulated experiments.

Keywords

• Distributed data processing
• machine learning
• aviation data analysis