Confidence interval prediction of ANN estimated LPT parameters

Yildirim, M.T.; Bulent, Kurt

doi:10.1108/aeat-10-2018-0266

Cited by 7 publications

(4 citation statements)

References 27 publications

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“…Flight data have many applications in aviation operation safety research [2][3][4][5][6]. Some scholars have applied flight data to turbine fault diagnosis, general aviation anomaly detection, aviation safety key landing index prediction [7][8][9][10][11], tower flight data manager man-machine system integration design processes, and new methods for nonlinear aerodynamic modeling of flight data [12][13][14]. Some scholars also analyze the flight characteristics of QAR data for landing at high-altitude airports, and use it for airline flight data monitoring machine learning methods, generating new operational safety knowledge from existing data, safety science insights gained from black-box-to-flight data monitoring, composite fault diagnosis using optimized MCKD and sparse representation of rolling bearings, rolling elements based on VMD, and sensitivity MCKD fault diagnosis, etc.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…Yildirim and Kurt [11] used neural networks and other methods in engine fault diagnosis and aircraft health management, and excellent results have been achieved. Francisco and colleagues [12] had applied a data mining technique for the generation of an information system's data model, which helps regulate the flight time and pilot scheduling. Reynolds and colleagues [13] had made the effective management for air traffic control and pilots via flight data analysis.…”

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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“…For which H ∞ technique is used for the purpose of stability as well as the consistency, theory of bi-index is used for the designing of FTC system. Many other algorithms and methodologies such as Kalman Filter SMO (Zhang et al , 2016; Djeghali et al , 2016), NNs (Chen et al , 2016; Taimoor and Aijun, 2019; Allen et al , 2016; Baghernezhad & horasani, 2016; Giorgi De et al , 2019; Fentaye et al , 2018; Yildirim and Kurt, 2019; Jia, and Duan, 2017; Amin et al , 2019; Amin et al , 2016; Taimoor and Aijun, 2020; Taimoor et al , 2020) and fuzzy logic (Ballesteros-Moncada et al , 2015) are implemented for the estimation of nonlinear parameters. In the above-mentioned techniques, NN techniques are better for faults identification because of the properties such as nonlinear function estimation property and learning abilities.…”

Section: Introductionmentioning

confidence: 99%

Adaptive rapid neural observer-based sensors fault diagnosis and reconstruction of quadrotor unmanned aerial vehicle

Taimoor

Maqsood³

et al. 2021

AEAT

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Purpose The objective of this research is to investigate various neural network (NN) observer techniques for sensors fault identification and diagnosis of nonlinear system in consideration of numerous faults, failures, uncertainties and disturbances. For the importunity of increasing the faults diagnosis and reconstruction preciseness, a new technique is used for modifying the weight parameters of NNs without enhancement of computational complexities. Design/methodology/approach Various techniques such as adaptive radial basis functions (ARBF), conventional radial basis functions, adaptive multi-layer perceptron, conventional multi-layer perceptron and extended state observer are presented. For increasing the fault detection preciseness, a new technique is used for updating the weight parameters of radial basis functions and multi-layer perceptron (MLP) without enhancement of computational complexities. Lyapunov stability theory and sliding-mode surface concepts are used for the weight-updating parameters. Based on the combination of these two concepts, the weight parameters of NNs are updated adaptively. The key purpose of utilization of adaptive weight is to enhance the detection of faults with high accuracy. Because of the online adaptation, the ARBF can detect various kinds of faults and failures such as simultaneous, incipient, intermittent and abrupt faults effectively. Results depict that the suggested algorithm (ARBF) demonstrates more confrontation to unknown disturbances, faults and system dynamics compared with other investigated techniques and techniques used in the literature. The proposed algorithms are investigated by the utilization of quadrotor unmanned aerial vehicle dynamics, which authenticate the efficiency of the suggested algorithm. Findings The proposed Lyapunov function theory and sliding-mode surface-based strategy are studied, which shows more efficiency to unknown faults, failures, uncertainties and disturbances compared with conventional approaches as well as techniques used in the literature. Practical implications For improvement of the system safety and for avoiding failure and damage, the rapid fault detection and isolation has a great significance; the proposed approaches in this research work guarantee the detection and reconstruction of unknown faults, which has a great significance for practical life. Originality/value In this research, two strategies such Lyapunov function theory and sliding-mode surface concept are used in combination for tuning the weight parameters of NNs adaptively. The main purpose of these strategies is the fault diagnosis and reconstruction with high accuracy in terms of shape as well as the magnitude of unknown faults. Results depict that the proposed strategy is more effective compared with techniques used in the literature.

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Confidence interval prediction of ANN estimated LPT parameters

Cited by 7 publications

References 27 publications

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

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

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

Adaptive rapid neural observer-based sensors fault diagnosis and reconstruction of quadrotor unmanned aerial vehicle

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