Identification method for support stiffness of whole aero-engine based on LSTM.

Based on the long short-term memory (LSTM) neural network, an identification method is proposed to identify the support stiffness of an aero-engine at rotating state. First, the dynamic model of a whole aero-engine with nonlinear support was established. The displacement responses corresponding to different support stiffnesses at the target rotating speed were obtained. Then, the deep leaning neural network with LSTM as the core layer was established. The network was trained with the displacement responses as the inputs and the support stiffnesses as the outputs. The nonlinea relationship between the support stiffnesses and the displacement responses was constructed. Finally, the support stiffness was directly identified with the generalization of the deep leaning network. The support stiffness of an aero-engine was identified by the proposed method. Results show that the recognition error is less than 2%, and the recognition accuracy of LSTM is better than that of the radial basis function neural network and support vector machine. The proposed method can avoid the complex optimization process in inverse dynamic problems and realize the identification of dynamic parameters for complex nonlinear structures. [ABSTRACT FROM AUTHOR]