APU performance parameter prediction model based on adaptive variation PSO-SVM

被引：1

作者：

Wang K. ^{[1
]}

Hou S. ^{[1
]}

Wang L. ^{[1
]}

机构：

[1] College of Electronic Information and Automation, Civil Aviation University of China, Tianjin

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2021年 / 43卷 / 02期

关键词：

Auxiliary power unit (APU); Particle swarm optimization (PSO) algorithm; Performance parameter prediction; Support vector machine (SVM);

D O I：

10.12305/j.issn.1001-506X.2021.02.27

中图分类号：

学科分类号：

摘要：

In order to improve the accuracy of performance parameter prediction of auxiliary power unit (APU), and solve the problem of parameter selection of support vector machine (SVM) model in practical use, adaptive mutation particle swarm optimization (PSO) is adopted to optimize the selection of penalty parameters and kernel parameters of SVM. And a prediction model of APU performance parameters based on adaptive mutation PSO algorithm is proposed. Furthermore, the influence of different prediction steps on short-term prediction accuracy is analyzed. The performance parameters of an APU are verified and compared with other prediction models. The experimental results show that for the prediction of exhaust gas temperature (EGT), the mean absolute percentage error (MAPE) of adaptive mutation PSO-SVM model is 47% lower than that of standard PSO-SVM model; for the prediction of oil temperature (OT), the MAPE of adaptive mutation PSO-SVM model is 29% lower than that of standard PSO-SVM, which provides some reference for short-term prediction of APU performance trend. © 2021, Editorial Office of Systems Engineering and Electronics. All right reserved.

引用

页码：526 / 536

页数：10

共 31 条

[1] ZHAO Y S, HU J, WU T Y, Et al., Performance simulation of large civil aircraft auxiliary power unit, Journal of Aerospace Power, 26, 7, pp. 1590-1598, (2011)
[2] FLORES J J, GRAFF M, RODRIGUES H., Evaluative design of ARMA and ANN models for time series forecasting, Renewable Energy, 44, pp. 225-230, (2012)
[3] WANG H, TANG Q D., Application for combination prediction method in apu condition monitoring, Aviation Maintenance & Engineering, 6, pp. 47-49, (2013)
[4] DONG P., Research on condition monitoring and maintenance decision of APU system, (2018)
[5] SONG Y X, PENG P, SHI Y S., Chaos arithmetic for civil aeroengine forecasting, Journal of Aerospace Power, 26, 3, pp. 675-679, (2011)
[6] DU W Z, WANG S., System identification and simulation of auxiliary power plant based on LSTM, Computer Measurement & Control, 28, 2, pp. 157-161, (2020)
[7] PI J, MA S, ZHANG Q Q, Et al., Aero-engine exhaust gas temperature prediction model based on IFOA-GRNN, Journal of Aerospace Power, 34, 1, pp. 8-17, (2019)
[8] DING G, XU M Q, HOU L G., Prediction of aeroengine exhaust gas temperature using process neural network, Journal of Aerospace Power, 24, 5, pp. 1035-1039, (2009)
[9] YILMAZ I., Evaluation of the relationship between exhaust gas temperature and operational parameters in CFM56-7 Bengines, Proceedings of the Institution of Mechanical Engineers, 223, 4, pp. 433-440, (2009)
[10] CHEN Q G, LI H W, LI M, Et al., Prediction of aeroengine exhaust gas temperature based on RBF neural networks, Journal of Ordnance Equipment Engineering, 40, 6, pp. 154-157, (2019)

← 1 2 3 4 →