基于无监督聚类与LSTM网络的航天器健康状态预测方法

Prediction Method of Spacecraft Health Status Based on Unsupervised Clustering and LSTM Networks Learning

健康状态预测是从系统层面保证航天器在轨安全稳定运行的关键技术.针对机电类关键部件存在性能退化过程的特点,提出一种基于无监督聚类与长短时记忆(LSTM)网络的航天器健康状态预测方法.该方法首先提取航天器单部件多维参数的高维时域特征,通过PCA方法将高维特征融合为反映部件运行状态的健康因子,再结合无监督聚类算法识别出故障的不同演化阶段,最后采用LSTM网络分别对各退化阶段构建其健康状态演化预测模型,实现对航天器部件健康状态预测.本文以控制系统关键部件控制力矩陀螺(CMG)为例对上述算法进行试验验证,验证了方法的有效性.

Health state prediction is a key technology to ensure the safe and stable operation of spacecraft in orbit from a system level.This paper proposes a method for predicting the health status of spacecraft based on unsupervised clustering and long short-term memory(LSTM)networks,in response to the characteristic of performance degradation in key components of mechatronics.This method first extracts high-dimensional time-domain features of multi-dimensional parameters of a single component of spacecraft,and fuses them into health factors that reflect the operational status of components through PCA method.Then,it combines unsupervised clustering algorithm to identify different evolution stages of faults.Finally,LSTM network is used to construct a health state evolution prediction model for each degradation stage,achieving health state prediction of spacecraft component.This article takes the key component of the control system,the Control Moment Gyroscope(CMG),as an example to experimentally verify the effectiveness of the above algorithm.