The landing safety prediction model by integrating pattern recognition and Markov chain with flight data

Abstract

This paper aims to predict the landing state during the landing phase to ensure landing safety and reduce the accidents loss. Some past researches have demonstrated the landing phase is the most dangerous phase in flight cycle and fatal accident. The landing safety problem has become a hot research problem in safety field. This study concentrates more on the prediction and advanced warning for landing safety. Firstly, four landing states are divided by three flight parameter variables including touchdown, vertical acceleration and distance to go; Subsequently, pattern recognition based on BP neural network is used to established the landing state prediction model; the genetic algorithm is used to initialize the model parameter; the Markov chain is proposed to revise and improve the model for higher prediction precision. Finally, in comparison of pattern recognition and the Markov chain revision results, the Markov chain revision method is demonstrated to be practical and effective.

Data-Driven Long-Landing Event Detection and Interpretability Analysis in Civil Aviation

“…The objective function of XGBoost is composed of two parts: training loss and regularization, as represented in the (5).…”

“…LLEs occur when the aircraft's landing distance lies beyond the normal range; it shortens the runway's available length, posing a significant risk. Even worse, the aircraft may run off if the pilots lack the necessary proficiency, leading to unpredictable consequences [4], [5]. Therefore, the detection of LLEs and their causes can help in preventing RE accidents, benefit airlines management and pilot training, and help in the development of aviation safety regulations.…”

Correction and Fitting Civil Aviation Flight Data EGT Based on RPM: Polynomial Least Squares Analysis

“…Revised the Markov chain to improve the model for higher prediction precision establish the landing safety prediction model by integrating pattern recognition and Markov chain with flight data. Massimo and colleagues [20] present new perspectives on the application of Artificial Intelligence (AI) solutions to process Spacecraft (S/C) flight data in order to augment currently used operational S/C health monitoring and diagnostics systems. Yao Li [21] used the Cessna172 flight simulator for flight data extraction to obtain an aerodynamic model, based on the idea of machine learning, a recurrent neural network was used to process multi-dimensional non-linear flight test data, and a real-time recursive learning algorithm was proved to be suitable for dynamic training, and some scholars have conducted combining multiple classifiers for the quantitative rank of abnormalities in-flight data, and applied in-flight data monitoring, flight control behavior analysis [22][23][24].…”