Aero-engine exhaust gas temperature prediction model based on IFOA-GRNN
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摘要: 利用广义回归神经网络(GRNN)良好的非线性映射能力,对航空发动机排气温度(EGT)进行预测。由于GRNN的预测性能受宽度系数的影响,因此采用改进的果蝇算法优化广义回归神经网络(IFOA-GRNN),并用优化后的GRNN对航空发动机的EGT进行预测。以某发动机为案例,选取相关参数作为预测模型的输入变量,EGT作为预测模型的输出变量。在相同的样本分配下,将FOA-GRNN(fruit fly optimization algorithm to optimize GRNN)、GRNN、自回归预测模型和优化的支持向量回归机作为对比算法。分析结果表明:IFOA-GRNN的收敛精度高于FOA-GRNN;IFOA-GRNN对EGT预测的平均相对误差为2.47%、拟合优度为0.8506,其预测效果均优于其他对比算法;同时,IFOA-GRNN对噪声的敏感性也低于其他对比算法。Abstract: General regression neural network (GRNN) has a good nonlinear mapping ability.So exhaust gas temperature (EGT) is predicted by GRNN.But, its accuracy of prediction is affected by the width coefficient of GRNN.To address the problem,the GRNN optimized by the improved fruit fly optimization algorithm (IFOA-GRNN) was proposed. And it was used to predict EGT.Taking the engine as an example, some parameters were taken as input variables and EGT taken as output variable of prediction models.The forecast results of IFOA-GRNN, FOA-GRNN(fruit fly optimization algorithm to optimize GRNN), GRNN,auto-regressive and optimized support vector regression were compared under the same training samples and testing samples.The experiment results showed that the convergence accuracy of IFOA-GRNN was higher than FOA-GRNN. Average relative error of IFOA-GRNN for EGT prediction was 2.47%,and the goodness of fit was 0.8506, the prediction effect of IFOA-GRNN was better than other comparison algorithms.And it was more accurate than other methods in the prediction of aero-engine exhaust gas temperature under noisy and no-noise conditions.
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[1] 薛薇,郭迎清,李睿.航空发动机状态监视、故障诊断研究及验证[J].推进技术,2011,32(2):271-275.XUE Wei,GUO Yingqing,LI Rui.Algorithm and experimental validation for condition monitoring,fault detection for gas turbine engine[J].Journal of Propulsion Technology,2011,32(2):271-275.(in Chinese) [2] 吕永乐,郎荣玲,路辉,等.航空发动机性能参数联合RBFPN和FAR预测[J].北京航空航天大学学报,2010,36(2):131-134.L Yongle,LANG Rongling,LU Hui,et al.Prediction of aeroengines performance parameter combining RBFPN and FAR[J].Journal of Beijing University of Aeronautics and Astronautics,2010,36(2):131-134.(in Chinese) [3] KUMAR A,SRIVASTAVA A,GOEL N,et al.Exhaust gas temperature data prediction by autoregressive models[C]∥Proceeding of the IEEE 28th Canadian Conference on Electrical and Computer Engineering.Halifax,Canada:IEEE,2015:976-981. [4] ILBAS M,TURKMEN M.Estimation of exhaust gas temperature using artificial neural network in turbofan engines[J].Journal of Thermal Science and Technology,2012,32(2):11-18. [5] YILMAZ I.Evaluation of the relationship between exhaust gas temperature and operational parameters in CFM56-7B engines[J].Proceedings of the Institution of Mechanical Engineers,2009,223(4):433-440. [6] 于广滨,丁刚,姚威,等.基于支持过程向量机的航空发动机排气温度预测[J].电机与控制学报,2013,17(8):30-36.YU Guangbin,DING Gang,YAO Wei,et al.Aeroengine exhaust gas temperature prediction using support process vector machine[J].Electric Machines and Control,2013,17(8):30-36.(in Chinese) [7] 钟诗胜,雷达,丁刚.卷积和离散过程神经网络及其在航空发动机排气温度预测中的应用[J].航空学报,2012,33(3):438-445.ZHONG Shisheng,LEI Da,DING Gang.Convolution sum discrete process neural network and its application in aeroengine exhausted gas temperature prediction[J].Acta Aeronautica et Astronautica Sinica,2012,33(3):438-445.(in Chinese) [8] 丁刚,徐敏强,侯立国.基于过程神经网络的航空发动机排气温度预测[J].航空动力学报,2009,24(5):1035-1039.DING Gang,XU Minqiang,HOU Liguo.Prediction of aeroengine exhaust gas temperature using process neural network[J].Journal of Aerospace Power,2009,24(5):1035-1039.(in Chinese) [9] 陈娇,王永泓,翁史烈.广义回归神经网络在燃气轮机排气温度传感器故障检测中的应用[J].中国电机工程学报,2009,29(32):92-97.CHEN Jiao,WANG Yonghong,WENG Shilie.Application of general regression neural network in fault detection of exhaust temperature sensors on gas turbines[J].Proceedings of the CSEE,2009,29(32):92-97.(in Chinese) [10] PANDA B N,BAHUBALENDRUNI V A R M,BISWAL B B.Optimization of resistance spot welding parameters using differential evolution algorithm and GRNN[C]∥Proceedings of 8th IEEE International Conference on Intelligent Systems and Control.Coimbatore,India:IEEE,2014:50-55. [11] 潘文超.果蝇最佳化演算法:最新演化式计算技术[M].台北:沧海书局,2011. [12] PAN W T.A new fruit fly optimization algorithm:taking the financial distress model as an example[J].Knowledge-Based Systems,2012,26:69-74. [13] LI Hongze,GUO Sen,LI Chunjie,et al.A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm[J].Knowledge-Based Systems,2013,37:378-387. [14] 王友卫,朱建明,凤丽洲.基于群密度的改进果蝇优化算法及在异常检测中的应用[J].工程科学与技术,2017,49(5):127-134.WANG Youwei,ZHU Jianming,FENG Lizhou.Improved fruit fly optimization algorithm based on population density and its application in anomaly detection[J].Advanced Engineering Sciences,2017,49(5):127-134.(in Chinese) [15] 唐鸣东,史秀志,周健,等.基于CFOA-GRNN的冲击地压危险等级预测[J].中国安全科学学报,2016,26(12):110-115.TANG Mingdong,SHI Xiuzhi,ZHOU Jian,et al.Prediction of rock-burst risk rating based on CFOA-GRNN network[J].China Safety Science Journal,2016,26(12):110-115.(in Chinese) [16] 李冬辉,尹海燕,郑博文.基于MFOA-GRNN模型的年电力负荷预测[J].电网技术,2018,42(2):585-590.LI Donghui,YIN Haiyan,ZHENG Bowen.An annual load forecasting model based on generalized regression neural network with multi-swarm fruit fly optimization algorithm[J].Power System Technology,2018,42(2):585-590.(in Chinese) [17] LI Penghua,HU Fangchao,LI Yingguo.Speaker identification based on gammatonecepstral coefficients and general regression neural network[C]∥Proceedings of Control and Decision Conference (2014 CCDC).Changsha,China:IEEE,2014:751-756. [18] TOMANDL D,SCHOBER A.Modified general regression neural network (MGRNN) with new efficient training algorithms as a robust black box-tool for data analysis[J].Neural Network,2001,14(8):1023-1034. [19] LI Hongze,GUO Sen,ZHAO Huiru,et al.Annual electric load forecasting by a least squares support vector machine with a fruit fly optimization algorithm[J].Energies,2012,5(11):4430-4445.
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