Multi-Fault Prediction of Electromechanical Actuators Based on SCI-BiGRU

This paper presents a fault prediction model for electromechanical actuators (EMA) based on the SCI-BiGRU architecture, with the aim of achieving precise prediction of multiple fault modes. Upon analyzing the monitoring signals of the electromechanical actuators, target signal with good distinguishability across various fault modes was selected. Based on the target signal, this study implements multi-fault prediction through a predict-then-classify approach. Firstly, an improved SCI temporal prediction module was introduced to forecast the future waveform of the target signal. Subsequently, effective fault features were extracted from the predicted signal and fed into an enhanced BiGRU model for accurate fault category diagnosis. This integrated process achieves precise prediction of multiple fault modes within a unified framework. Experimental validation was conducted on the FLEA dataset from NASA , where the proposed model demonstrated robust performance in both temporal prediction and fault classification stages, thereby achieving high-precision early prediction of multiple fault modes.