基于贝叶斯更新的机电作动器健康因子构建方法

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

机电作动器(EMA)由于重量轻、体积小、可靠性高等优势成为多电/全电飞机的核心部件,逐步广泛应用于各类多电/全电飞机中。然而,EMA动态变化的运行模式和载荷工况给其退化建模和健康因子(HI)估计带来较大挑战。因此,提出一种基于贝叶斯更新的EMAH1构建方法。首先基于历史监测数据构建HI先验模型,在此基础上,结合贝叶斯更新理论和实时监测数据对EMAHI先验模型参数分布进行迭代更新,最终实现不同运行模式和载荷工况下EMA退化状态的准确表征。为解决变工况条件下EMAHI构建模型失配问题提供了一种新颖的思路,并基于NASA公开数据集进行了实验验证。结果表明,与基于模型辨识的EMAHI构建方法相比,基于贝叶斯更新的EMAHI构建方法具有更强的工况适应能力,能够在变工况条件下有效地构建出EMAHI。

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.