Health indicator construction method based on Bayesian updating for electro-mechanical actuator

Abstract

Electro-mechanical actuator (EMA) has become the core components of more/all electric aircraft due to its light weight, small size, high reliability and etc., which has gradually been widely utilized in various types of more/all electric aircrafts. However, the dynamic operation profiles and load conditions of EMA bring great challenges to its degradation modeling and health indicator (HI) estimation. Therefore, an EMA HI construction method based on Bayesian updating is proposed. Firstly, an HI prior model is built based on historical monitoring data. Secondly, the EMA HI prior model parameters are updated iteratively through utilizing Bayesian updating theory and real-time monitoring data. Finally, the EMA degradation state under various operation profiles and load conditions is accurately characterized. This study provides a novel idea for solving the mismatch issue of the EMA HI construction model under variable working conditions, and experiments with NASA benchmark data verify the effectiveness of the proposed method. The experiment results show that compared with the EMA HI construction method based on model identification, the proposed EMA HI construction method based on Bayesian updating has stronger adaptability to the working conditions and can effectively construct EMA HI under variable working conditions.