| Citation: | DAI Jinling, XU Aiqiang, YU Chao, et al. Online prediction model of the state of engine based on multivariate KELM[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(11): 2277-2286. doi: 10.13700/j.bh.1001-5965.2020.0389(in Chinese)
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In order to solve the problem that the state changes of only one variable instead of related variables are considered in the process of aircraft engine condition prediction, an online prediction model of the state of engine based on multivariate Kernel Extreme Learning Machine (KELM) is proposed. First, the phase space reconstruction of multivariable time series is used to transform the temporal correlation into the spatial correlation. Then, by studying the relationship between KELM and the Kernel Recursive Least Squares (KRLS), KRLS is extended into the online sparse KELM framework. Finally, the samples are made sparse by using approximate linear dependence to control the growth of network structure, and ultimately online prediction of multivariable nonstationary series is realized. The prediction results of engine flight parameters of a certain trainer show that, compared with KB-IELM, NOS-KELM and FF-OSKELM in the premise of online prediction, the prediction accuracy is decreased by 90.61%, 58.14% and 25.77% respectively, and the prediction stability is decreased by 99.61%, 75.03% and 28.59% respectively, with higher prediction accuracy and stability. All methods get best results with multivariate inputs, which also proves thatthe consideration of multivariable state factors is of great significance to the online prediction of single variable as well.
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