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

Nongtian Chen,Chong Peng,Youchao Sun,Zongpeng Wang

doi:10.3390/app12052545

Abstract

There are different missing flight data due to various reasons in the process of acquisition and storage, especially in general aviation, which cause inconvenience for flight data analysis. Effectively explaining the relationship between flight data parameters and selecting a simple and effective method for fitting and correcting flight data suitable for engineering applications are the main points of the paper. Herein, a convenient and applicable approach of missing data correction and fitting based on the least squares polynomial method is introduced in this work. Firstly, the polynomial fitting model based on the least squares method is used to establish multi-order polynomial by existing flight data since the order of the least squares polynomial has a direct impact on the fitting effect. The order is too high or too small, over-fitting or deviation will occur, resulting in improper data. Therefore, the optimization and selection of the model order are significant for flight data correction and fitting. Because the flight data of the aircraft engine exhaust gas temperature (EGT) are often lost because of the immature detection technology, a series of the multi-order polynomial are established by the relationship of aircraft engine exhaust gas temperature and Revolutions Per Minute (RPM). Case study results confirm the optimal model order is four for the fitting and correction of aircraft engine exhaust temperature, and the least squares polynomial method is applicable and effective for EGT flight data correction and fitting based on RPM data.

Highlights

Flight data is playing an increasingly important role in data-driven civil aviation safety management, especially in the application of flight operation quality assurance (FOQA) [1], aircraft fault diagnosis [2], runway safety analysis [3], airline safety management [4], flight performance analysis [5], which strongly promotes the construction of smart civil aviation, the big data applications play an increasingly important role [6–9]
In the process of collecting and recording flight data, some data are lost at a certain point due to various reasons, which will result in a breakpoint in the data curve
Sci. 2022, 12, 2545 often lost or numerical deviation because of the immature detection technology in the general aviation, the polynomial fitting method based on least squares method is used to fit and correct the missing flight data for the relationship of aircraft engine exhaust temperature (EGT) and aircraft engine revolutions per minute (RPM)

Summary

Introduction

Flight data is playing an increasingly important role in data-driven civil aviation safety management, especially in the application of flight operation quality assurance (FOQA) [1], aircraft fault diagnosis [2], runway safety analysis [3], airline safety management [4], flight performance analysis [5], which strongly promotes the construction of smart civil aviation, the big data applications play an increasingly important role [6–9]. 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 [22–24]. Sci. 2022, 12, 2545 often lost or numerical deviation because of the immature detection technology in the general aviation, the polynomial fitting method based on least squares method is used to fit and correct the missing flight data for the relationship of aircraft engine exhaust temperature (EGT) and aircraft engine revolutions per minute (RPM). Since the predicted deviation gradually increases away from the current data segment, the estimated deviation is gradually increased, so usually seeking out the most suitable fitting polynomial order through the numerical calculation of the fitting equation of the polynomial and the minimum deviation analysis

Calculation Model and Order Selection

Case Study

Discussion

Conclusions