A Transfer Learning Based Unmanned Aerial Vehicle MEMS Inertial Sensors Fault Diagnosis Method

Abstract

In this paper, we propose a novel transfer learning based micro-electromechanical system (MEMS) inertial sensors fault diagnosis method. First, the MEMS inertial sensors fault diagnosis method is formulated to a deep transfer learning problem in which the offline samples are deemed as source domain and the online samples are set to target domain features. Second, the bidirectional long short-term memory and Hilbert-Huang transformation-based feature transfer model is designed to decrease the discrepancy between SD and TD, that performs the transfer operation using intrinsic mode function features. Then we propose a convolutional neuro network-based transfer learning algorithm to further decrease deep features discrepancy and perform the fault classification tasks on TD. According to the experiments, the proposed FD method has achieved excellent fault classification performance and significantly improvement comparing with the state-of-the art methods.