Application of Neural Network Based on Real-Time Recursive Learning and Kalman Filter in Flight Data Identification

Li, Yao; Si, Haiqing; Zong, Yitong; Wu, Xiao-Jun; Zhang, Peihong; Jia, Hongyin; Xu, Shuqing; Tang, Dayong

doi:10.1007/s42405-021-00380-0

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…Usually, due to the influence of objective factors such as weather, the flight data recorded by the flight data recorder will contain noise, and the recorded parameters have different magnitudes, so it is necessary to perform noise reduction and standardized data pre-processing for flight training multiple time series, and Kalman filter is used as the noise reduction method in this experiment. The experiments of Gite [22] and Li [23] show that the flight data are smoother and more realistic after Kalman filtering, so Kalman filtering is adopted as the noise reduction method in this experiment.…”

Section: A Data Preprocessing and Experimental Datamentioning

confidence: 99%

Flight Training Subject Identification Method Based on Multivariate Subsequence Search With Double Windows

Deng

Ren

et al. 2023

IEEE Access

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To solve the problem of low completeness in identifying flight training subjects in flight training data, a Multivariate Subsequence Search with Double Windows (MSDW) algorithm is proposed based on the Euclidean distance. First, a method for retrieving locally optimal subsequences is proposed based on constructing a distance matrix of candidate subsequences; then, the method is applied to the MSDW algorithm for retrieving all eligible locally optimal subsequences; finally, a double windows method is employed to improve the completeness of matched subsequences. Theoretical analysis and experimental results show that the MSDW algorithm is well designed and can correctly and effectively retrieve the local optimal subsequence, which can achieve recognition accuracy of 1 in the engineering application of single flight training subject recognition. It is experimentally verified that compared with the traditional SPRING algorithm, the completeness of the MSDW algorithm in recognizing individual flight training subjects is greatly improved, and the effectiveness of flight training subject recognition is effectively enhanced.INDEX TERMS Dynamic time warping, euclidean distance, flight training subject identification, multivariate time series, subsequence similarity retrieval.

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Section: A Data Preprocessing and Experimental Datamentioning

confidence: 99%

Flight Training Subject Identification Method Based on Multivariate Subsequence Search With Double Windows

Deng

Ren

et al. 2023

IEEE Access

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“…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]. In terms of flight data analysis and application research, make the outlier detections with uncertain data from flight data for pilot performance and maintenance assessment [25,26].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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“…Liu [18] proposed a method for selecting a safe landing site by applying the optimization problem. In addition, methods for finding craters through machine learning and deep learning are being studied for hazard avoidance or real-time evaluation [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Prediction and Validation of Landing Stability of a Lunar Lander by a Classification Map Based on Touchdown Landing Dynamics’ Simulation Considering Soft Ground

et al. 2021

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In this paper, a method for predicting the landing stability of a lunar lander by a classification map of the landing stability is proposed, considering the soft soil characteristics and the slope angle of the lunar surface. First, the landing stability condition in terms of the safe (=stable), sliding (=unstable), and tip-over (=statically unstable) possibilities was checked by dropping a lunar lander onto flat lunar surfaces through finite-element (FE) simulation according to the slope angle, friction coefficient, and soft/rigid ground, while the vertical touchdown velocity was maintained at 3 m/s. All of the simulation results were classified by a classification map with the aid of logistic regression, a machine-learning classification algorithm. Finally, the landing stability status was efficiently predicted by Monte Carlo (MC) simulation by just referring to the classification map for 10,000 input datasets, consisting of the friction coefficient, slope angles, and rigid/soft ground. To demonstrate the performance, two virtual lunar surfaces were employed based on a 3D terrain map of the LRO mission. Then, the landing stability was validated through landing simulation of an FE model of a lunar lander requiring high computation cost. The prediction results showed excellent agreement with those of landing simulations with a negligible computational cost of around a few seconds.

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Application of Neural Network Based on Real-Time Recursive Learning and Kalman Filter in Flight Data Identification

Cited by 7 publications

References 14 publications

Flight Training Subject Identification Method Based on Multivariate Subsequence Search With Double Windows

Flight Training Subject Identification Method Based on Multivariate Subsequence Search With Double Windows

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

Prediction and Validation of Landing Stability of a Lunar Lander by a Classification Map Based on Touchdown Landing Dynamics’ Simulation Considering Soft Ground

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