QAR Data Research Articles

An Improved Transformer Method for Prediction of Aircraft Hard Landing Based on QAR Data

An Improved Transformer Method for Prediction of Aircraft Hard Landing Based on QAR Data

Identification of Airline Turbulence Using WOA-CatBoost Algorithm in Airborne Quick Access Record (QAR) Data

Turbulence is a significant operational aviation safety hazard during all phases of flight. There is an urgent need for a method of airline turbulence identification in aviation systems to avoid turbulence hazards to aircraft during flight. Integrating flight data and machine learning significantly enhances the efficacy of turbulence identification. Nevertheless, present studies encounter issues including unstable model performance, challenges in data feature extraction, and parameter optimization. Hence, it is imperative to propose a superior approach to enhance the accuracy of turbulence identification along airline. The paper presents a combined swarm intelligence and machine learning model based on data mining for identifying airline turbulence. Based on the theory of swarm-intelligence-based optimization algorithm, the optimal parameters of Categorical Boosting (CatBoost) are obtained by introducing the whale optimization algorithm (WOA), and the corresponding WOA-CatBoost fusion model is established. Then, the Recursive Feature Elimination algorithm (RFE) is used to eliminate the data with lower feature weights, extract the effective features of the data, and the combination with the WOA brings robust optimization effects, whereby the accuracy of CatBoost increased by 11%. The WOA-CatBoost model can perform accurate turbulence identification from QAR data, comparable to that with established EDR approaches and outperforms traditional machine learning models. This discovery highlights the effectiveness of combining swarm intelligence and machine learning algorithms in turbulence monitoring systems to improve aviation safety.

QAR Data Imputation Using Generative Adversarial Network with Self-Attention Mechanism

QAR Data Imputation Using Generative Adversarial Network with Self-Attention Mechanism

Research on fuel consumption calculation based on QAR and ACR data

Research on fuel consumption calculation based on QAR and ACR data

Aviation Safety Risk Analysis Based on Bayesian Network Modeling

The occurrence of serious flight accidents not only brings huge economic losses to airlines but also poses a great threat to passengers' lives. The occurrence of serious flight accidents will not only bring great economic losses to airlines but also cause great threats to the lives of passengers. Therefore, in this paper, based on the flight records of different flight crews, flight routes, airports, and specific flight conditions, aircraft safety risk analysis is conducted to calculate and evaluate the risk propensity. Firstly, the data are preprocessed, and then the QAR data are clustered to extract the key data items affecting the safe flight of the aircraft; then the data are transformed to the attached data, and a hierarchical clustering model is established based on the K-means clustering algorithm, which classifies the factors affecting the safety quality of the aircraft flight into the three main aspects of environmental factors, crew maneuvering, and aircraft state, and the key parameters are weighted by the Random Forest algorithm. weights of the key parameters. Then, based on the 3 criterion, the outliers exceeding 3 were selected as the dependent variables leading to the occurrence of deviation; based on the three-layer model, a Bayesian neural network model based on the three-layer model, and then determine the Bayesian nodes; finally, calculate the Bayesian node probability, and then provide a reasonable quantitative description of the flight maneuver.

Hard Landing Pattern Recognition and Precaution With QAR Data by Functional Data Analysis

Hard Landing Pattern Recognition and Precaution With QAR Data by Functional Data Analysis

A Review of the Research of Quick Access Recorder data

In recent years, QAR data has been widely concerned by scholars because of its reliability and integrity. QAR data has become an important basis for flight quality monitoring, engine status detection, aircraft system failure diagnosis, 3D animation route design and other aspects of various airlines in the world. The keywords of research on QAR data in China and other countries were clustering analyzed by VOSviewer, the hot spots were introduced, and research were summarized and discussed from five aspects. At last, some shortcomings of current research on QAR data were pointed out and some future development directions were presented.

A Method for Aero-Engine Gas Path Anomaly Detection Based on Markov Transition Field and Multi-LSTM

There are some problems such as uncertain thresholds, high dimension of monitoring parameters and unclear parameter relationships in the anomaly detection of aero-engine gas path. These problems make it difficult for the high accuracy of anomaly detection. In order to improve the accuracy of aero-engine gas path anomaly detection, a method based on Markov Transition Field and LSTM is proposed in this paper. The correlation among high-dimensional QAR data is obtained based on Markov Transition Field and hierarchical clustering. According to the correlation analysis of high-dimensional QAR data, a multi-input and multi-output LSTM network is constructed to realize one-step rolling prediction. A Gaussian mixture model of the residuals between predicted value and true value is constructed. The three-sigma rule is applied to detect outliers based on the Gaussian mixture model of the residuals. The experimental results show that the proposed method has high accuracy for aero-engine gas path anomaly detection.

Airworthiness compliance verification method of CO2 metric based on QAR data

Airworthiness compliance verification method of CO2 metric based on QAR data

Flight visual simulation system based on sensor technology and QAR data

With the development of wireless sensor technology, more and more fields are applied to wireless sensor technology, and the design and implementation of the flight scene system are in it. The purpose of this study is to design and implement a flight visual simulation system using sensor technology and QAR data. The method of this study is to first establish the mathematical model of the aircraft and then calculate the aerodynamic force and torque of each part of the aircraft respectively to obtain the nonlinear dynamic model of the aircraft, and finally realize the whole simulation model on the MATLAB platform. The results show that the main motion variables corresponding to the conjugate complex root (0.0530.287i) are the velocity and pitch angle of the aircraft, and they all change slowly and long period. The period is 21.9 seconds, and the frequency doubling time (LN2 / 0.053 + 0.287i) is 11.0 seconds, which meets the requirements of flight quality specification. It is concluded that the flight visual simulation system in this study can well describe the flight trajectory and observe the external scene. The simulation system uses computer graphics and image technology to realize real-time and accurate simulation and reproduction of the flight status and trajectory of the aircraft, which can provide an intuitive and convenient simulation analysis method for the exploration of new aerospace technology and engineering design.

Aircraft Departure Fuel Consumption Improvement Model Based On Flight Data

Accurate assessment of aircraft fuel consumption is an important means for airlines to reduce flight costs and control fuel emissions. By analyzing the actual flight data of QAR, the functional relationship between the aerodynamic parameters in the fuel consumption model of the departure climb phase is fitted, and the crosswind is added to the fuel consumption assessment model. Based on the analysis of the departure training of Qingdao Airport, the fuel consumption model of the exit climbing stage was calculated using the improved fuel consumption model. The results showed that the calculation accuracy of the wind tunnel experiment was improved by 3.6%, and the theory of the fuel evaluation experiment considering the crosswind was considered. The accuracy of the model calculation and QAR data is 93.3%.

Fuel savings model after aero-engine washing based on convolutional neural network prediction

In order to accurately calculate the fuel savings after aero-engine washing, therefore the engine cleaning process flow can be arranged more scientifically and reasonably. In this paper, a derivation method of fuel savings model after engine washing based on delta fuel flow (DFF) degradation baseline and convolutional neural network prediction technology is proposed. Firstly, the fuel savings model of cruise phase is established based on gas path parameters related to the engine fuel consumption, and the QAR data’s trend on entire flight is mined, and the regularity of the gas path parameters in the cruise phase is extended to the entire flight, thereby the fuel savings model after engine washing of whole Flight is derived. In the model derivation process, the unwashed engine’s DFF degradation baseline after the wash point is predicted. According to the characteristics of DFF discrete time series, a discrete input process neural network prediction model based on discrete data convolution operation is established. The convolution operation of discrete data is used to replace the integral operation of continuous function, which effectively avoids the loss of fitting precision caused by the smooth processing of discrete time series data before the input of the process neural network model, and improves the prediction accuracy of discrete time series data. A denoising method combining Singular Value Decomposition (SVD) and Empirical Mode Decomposition (EMD) is used to denoise the DFF before and after engine washing, and the trend analysis method is used to solve the intersection of the unwashed state engine’s DFF degradation baseline and washed state engine’s DFF degradation baseline, thereby the washing effect cycle numbers is predicted, and in which the total fuel savings model of whole fleet after engine washing is derived by accumulating every flight fuel savings. The post-washed engine fuel saving calculation of an airline is used as a verification experiment. The results show that post-washed engine fuel saving prediction model presented in the paper is the closest to the actual fuel savings model.

Application architecture and typical cases of big data technology in health management of civil aircraft system

Under the background of the big data era, the concept and technology of civil aircraft health management have been continuously innovated. This paper investigates the progress of health management technologies of mainstream manufacturers and airlines. Based on the analysis of operational data, this paper proposes a big data platform architecture for civil health management applications. Besides, the case of applying QAR data for prediction is introduced in the typical fault of air conditioning system, and the effectiveness of AR model in short-term trend prediction is verified.

Research on Unstable Approach Detection of Civil Aviation Aircraft

In order to avoid serious civil aviation accidents caused by low altitude and unstable approach, the empirical model of unstable approach below 1000 feet is established by means of pilot survey. By using AGS decoding platform, unstable events are triggered. With Python, the real values of trigger time are extracted and stored and then compare and analyze the data. Experiments are conducted on 30,000 actual QAR data of B-737 aircraft. The results showed that 3100 unstable approach segments were triggered, and the ratio was about 10.3%. Two of the main factors causing unstable approach are the overrun of air speed and the deviation of localizer. Through the study and analysis of various factors of model, preventive measures can be taken in order to reduce the incidence of unstable approach.

Study on Anomaly Detection Algorithm of QAR Data Based on Attribute Support of Rough Set

According to the characteristics of the large amount of QAR data，such as many parameters, time constraints, strong randomness and the problems of discrete data, together with attribute reduction and rules colleting during QAR anomaly detection, the paper proposed a anomaly detection algorithm of QAR data based on attribute support of rough set. Firstly, we discrete QAR data after converting the time sequence data into non-time sequence information system based on attribute support. Second, we carry on the attribute reduction using difference matrix based on statistics. At last, we get the fault rules with the decision tables based on fault occurrence statistics of the decision attributes. The method not only primely retains the time characteristics of QAR data, but also strengthens the relation between condition attributes and decision attributes. The efficiency of attribute reduction and rules extraction is high and the amount of calculation is small. The experimental results show that the method is feasible, reliable and availability.

Remaining Useful Life Estimation Method for the Turbine Blade of a Civil Aircraft Engine Based on the QAR and Field Failure Data

Remaining Useful Life Estimation Method for the Turbine Blade of a Civil Aircraft Engine Based on the QAR and Field Failure Data

The Design of Avionics System Interfaces Emulation and Verification Platform Based on QAR Data

In order to avoid the complex mathematical modeling and ensure the reliability of avionics system verification, this paper has designed an interfaces emulation and verification platform of avionics system based on QAR data. Platform includes 2 parts: Emulator and Simulator. Simulator generates the flight environmental data which is come from QAR and transforms the data into excitation signal of devices. Emulator emulates the interface features of avionic devices according to the ICD and can be replaced with real devices. By comparing the actual input-output data of devices with QAR theoretical data, this platform can evaluate the running performance of avionic systems or devices and the rationality of the ICD.

A Novel Probability Density Estimating Method for Relative Altitudes of Civil

With the rapid development of civil aviation, the shortage of airspace resource is becoming one of the main limiting factors for air traffic transportation. Inspired by the ideas of reduced vertical separation minimum and reducing aircraft separation standard, this paper analyzes the distributing characteristics of civil aircraft tracks in cruising level space and presents a novel method for probability density estimation of civil aircraft tracks' relative altitudes in their cruising flight level spaces, and analyzes the distributing characteristics at their cruising altitude direction.The method is based on QAR data of enough random civil aircraft tracks from a domestic airline. First, we choose altitude data of each track's main cruise phase and calculate altitude values relative to the standard cruise altitude of respective cruising flight level, we call these processes as spatial alignment and the relative altitudes as relative-cruising altitudes or relative-cruise altitudes. Secondly, we do temporal alignment and figure out the relative altitude values of these tracks in panel data form. Then, we estimate the probability density of the relative altitude values using kernel density estimate method, with the least square cross-validation method choosing asymptotically optimal smoothing parameters. Finally, we figure out different confidence track bands, namely confidence interval limits, and do analysis.All the analysis results show that there is a great deal of airspace margin and space resource potential in each cruising levels. Hence there are a great possibility and expectation that the vertical separation minimum can be reduced again and add flight levels, which is beneficial to increase air traffic capacity and relieve traffic jams.

Research On QAR Data Mining Method Based On Improved Association Rule

Abstract The apriori scans database many times and generates large candidate itemsets so that its I/O performance and efficiency seriously affect the universal application and promotion. In order to solve the problem, the paper proposed a method by seting a unique number and recording the location of each itemset. It scans the database once and never generates the huge candidate itemsets and has been applied to QAR data. Experiments show that the proposed algorithm is capable of discovering meaningful and useful association rules in an effective manner, speeding up less execution time.

An Overview of the Effect of GNSS Operation at High Elevation Airport

It analyzes difficulties faced by flight operation in high elevation airport. It puts forward solutions for flight operation on plateau. It analyzes the effect of GNSS in several new navigation technology used in flight operation in high elevation airport, such as RNP AR, and ADS-B, and RVSM, and EFVS/SVS, and EGPWS technology. QAR data is used to analyze the cross track error and height error of a flight on plateau airway.