Improved LS-SVM Method for Flight Data Fitting of Civil Aircraft Flying at High Plateau

Abstract

High-plateau flight safety is an important research hotspot in the field of civil aviation transportation safety science. Complete and accurate high-plateau flight data are beneficial for effectively assessing and improving the flight status of civil aviation aircrafts, and can play an important role in carrying out high-plateau operation safety risk analysis. Due to various reasons, such as low temperature and low pressure in the harsh environment of high-plateau flights, the abnormality or loss of the quick access recorder (QAR) data affects the flight data processing and analysis results to a certain extent. In order to effectively solve this problem, an improved least squares support vector machines method is proposed. Firstly, the entropy weight method is used to obtain the index weights. Secondly, the principal component analysis method is used for dimensionality reduction. Finally, the data are fitted and repaired by selecting appropriate eigenvalues through multiple tests based on the LS-SVM. In order to verify the effectiveness of this method, the QAR data related to multiple real plateau flights are used for testing and comparing with the improved method for verification. The fitting results show that the error measurement index mean absolute error of the average error accuracy is more than 90%, and the error index value equal coefficient reaches a high fit degree of 0.99, which proves that the improved least squares support vector machines machine learning model can fit and supplement the missing QAR data in the plateau area through historical flight data to effectively meet application needs.

Generation of Guidance Commands for Civil Aircraft to Execute RNP AR Approach Procedure at High Plateau

“…The TF segment is a great circle route, which is defined by the starting point P 1 (L 1 , λ 1 , h 1 ) and the ending point P 2 (L 2 , λ 2 , h 2 ), and the spatial position is determined, where L is the latitude, λ is the longitude, and h is the altitude. The azimuth angle of the TF segment is unequal everywhere, so the guidance law of the TF segment is given as Equation (1).…”

“…High-precision terminal operation at high-plateau airports is a worldwide challenge, especially for China's civil aviation transportation industry [1]. A high-plateau airport refers to an airport with an altitude of more than 8000 feet (about 2438 m), which has low air density and atmospheric pressure, complex and harsh terrain, poor airport clearance, and so on [2].…”

“…The RNP AR approach procedure is a high-performance RNP procedure, which is of great value for ensuring take-off and landing safety of civil aircraft at high-plateau airports or some special airports [6,7]. The advantages of RNP AR can be summarized as follows: 1 It can effectively reduce the impact of obstacles via a flexible horizontal trajectory to increase the take-off weight [8]. 2 Precise flight guidance can improve the airspace capacity and efficiency and reduce fuel consumption and exhaust emissions [9][10][11].…”

Risk assessment method for controlled flight into terrain of airlines based on QAR data

“…Currently, scholars use QAR data to conduct research primarily in the following domains: Mathematical analysis and processing methods to estimate data parameters that are not directly recorded in QAR data (Sembiring et al , 2013; Sembiring et al , 2018), determination of energy dissipation rate (EDR) through wind component of QAR data and pollutant emissions during flight (Kim et al , 2022; Huang et al , 2019; Pan et al , 2021), identification of normal and abnormal flights during operation (Li et al , 2011), identification of anomalous in-flight data (Chen et al , 2022; Jesse et al , 2008), consideration of the tradeoff between accuracy and complexity in Engine Health Management (Ren et al , 2022), resolution of the similarity issue in QAR data (Feng et al , 2012), diagnosis of aircraft exceedance event (Gao et al , 2014) and enhancement of maintenance fault diagnosis and prevention efficiency (Yang and Dong, 2012; Yang and Meng, 2012). Diverse data analysis systems are developed to explore operational risks, evaluate and assess pilot flight technology, identify technical training problems, optimize training processes, enhance flight training quality, monitor flight quality, engine status and elevate the underutilized QAR data rate (Haverdings and Chan, 2010; Sun, 2003; Li, 2010).…”

“…Currently, scholars use QAR data to conduct research primarily in the following domains: Mathematical analysis and processing methods to estimate data parameters that are not directly recorded in QAR data (Sembiring et al, 2013;Sembiring et al, 2018), determination of energy dissipation rate (EDR) through wind component of QAR data and pollutant emissions during flight (Kim et al, 2022;Huang et al, 2019;Pan et al, 2021), identification of normal and abnormal flights during operation (Li et al, 2011), identification of anomalous in-flight data (Chen et al, 2022;Jesse et al, 2008), consideration of the tradeoff between accuracy and complexity in Engine Health Management (Ren et al, 2022), resolution of the similarity issue in QAR data (Feng et al, 2012), diagnosis of aircraft exceedance event (Gao et al, 2014) and enhancement of maintenance fault diagnosis and prevention efficiency (Yang and Dong, 2012;Yang and Meng, 2012).…”