A Novel Data-Driven-Based Component Map Generation Method for Transient Aero-Engine Performance Adaptation

Zhou, Wenxiang; Lu, Sangwei; Huang, Jinquan; Pan, Muxuan; Chen, Zhong‐Guang

doi:10.3390/aerospace9080442

Cited by 6 publications

(3 citation statements)

References 36 publications

(43 reference statements)

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“…Under different observation conditions, the radar stealth features of aircraft at different sweep angles have been learned [ 29 ]. Two particle search algorithm is established to optimize the RCS index of the vertical tail [ 31 ]. Multi-platform radar detection technology has wide applications in reconnaissance, terrain tasks, meteorological parameters and hydrological monitoring [ [32] , [33] , [34] ].…”

Section: Introductionmentioning

confidence: 99%

Facilitating learning adaptive V-tail of a supersonic missile for radar cross-section reduction during interval flight

Zhou,

Huang

2023

Heliyon

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Section: Introductionmentioning

confidence: 99%

Facilitating learning adaptive V-tail of a supersonic missile for radar cross-section reduction during interval flight

Zhou,

Huang

2023

Heliyon

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“…Zhou used 200 sets of initial training data and found that the algorithm has a faster convergence rate compared to the traditional training algorithm. After steady-state adaptation, the maximum absolute measurement deviation was reduced from 6.35% to 0.44% [17]. Ying proposed a new method of compressor performance modeling based on an SVM nonlinear regression algorithm to overcome the difficulty of having only some of the design operating points in the compressor characteristic map and found that the extrapolation performance of ordinary SVM was better than that of BPNN [18].…”

Section: Introductionmentioning

confidence: 99%

Compressor Performance Prediction Based on the Interpolation Method and Support Vector Machine

et al. 2023

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Compressors are important components in various power systems in the field of energy and power. In practical applications, compressors often operate under non-design conditions. Therefore, accurate calculation on performance under various operating conditions is of great significance for the development and application of certain power systems equipped with compressors. To calculate and predict the performance of a compressor under all operating conditions through limited data, the interpolation method was combined with a support vector machine (SVM). Based on the known data points of compressor design conditions, the interpolation method was adopted to obtain training samples of the SVM. In the calculation process, preliminary screening was conducted on the kernel functions of the SVM. Two interpolation methods, including linear interpolation and cubic spline interpolation, were used to obtain sample data. In the subsequent training process of the SVM, the genetic algorithm (GA) was used to optimize its parameters. After training, the available data were compared with the predicted data of the SVM. The results show that the SVM uses the Gaussian kernel function to achieve the highest prediction accuracy. The prediction accuracy of the SVM trained with the data obtained from linear interpolation was higher than that of cubic spline interpolation. Compared with the back propagation neural network optimized by the genetic algorithm (GA-BPNN), the genetic algorithm optimization of extreme learning machine neural network (GA-ELMNN), and the genetic algorithm optimization of generalized regression neural network (GA-GRNN), the support vector machine optimized by the genetic algorithm (GA-SVM) has a better generalization, and GA-SVM is more accurate in predicting boundary data than the GA-BPNN. In addition, reducing the number of original data points still enables the GA-SVM to maintain a high level of predictive accuracy.

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“…Accurate prediction of engine EGT is essential to maintain engine operation, and an accurate engine EGT baseline model is the basis of engine gas path performance monitoring and fault diagnosis. Therefore, accurate and reliable engine EGT baseline modeling method is required [7].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Exhaust Gas Temperature Baseline Predictions for Aeroengine Based on Machine Learning Algorithms

Wang

Zhao

2022

Aerospace

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The exhaust gas temperature (EGT) baseline of an aeroengine is key to accurately analyzing engine health, formulating maintenance decisions and ensuring flight safety. However, due to the complex performance characteristics of aeroengine and the constraints of many external factors, it is difficult to obtain accurate non-linear features between various operating factors and EGT. In order to diagnose and forecast aeroengine performance quickly and accurately, four data-driven baseline prediction frameworks for EGT are proposed. These baseline frameworks took engine operating conditions and operating state control parameters as input variables and EGT as predicted output variables. The original data were collected from CFM56-5B engine ACARS flight data. Four typical machine learning methods, including Generalized Regression Neural Network (GRNN), Radial Basis Neural Network (RBF), Support Vector Regression (SVR) and Random Forest (RF) are trained to develop the models. Four aeroengine EGT baseline models were validated by comparing the after-flight data of another engine. The results show that the developed GRNN models have the best accuracy and computational efficiency compared with other models, and their RE and CPU calculation time on the verification set are 1.132 × 10−3 and 3.512 × 10−3 s, respectively. The developed baseline prediction frameworks can meet the needs of practical engineering applications for airlines. The methodologies developed can be employed by airlines to predict the EGT baseline for the purpose of engine performance monitoring and health management.

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A Novel Data-Driven-Based Component Map Generation Method for Transient Aero-Engine Performance Adaptation

Cited by 6 publications

References 36 publications

Facilitating learning adaptive V-tail of a supersonic missile for radar cross-section reduction during interval flight

Facilitating learning adaptive V-tail of a supersonic missile for radar cross-section reduction during interval flight

Compressor Performance Prediction Based on the Interpolation Method and Support Vector Machine

Data-Driven Exhaust Gas Temperature Baseline Predictions for Aeroengine Based on Machine Learning Algorithms

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