基于数据关联性分析的飞轮异常检测

针对航天器早期故障在闭环系统下难以被检测、数学模型难以精确建立的问题,提出了一种基于数据关联性分析的归纳式系统异常监测(IMS)方法。该方法采用无监督学习的聚类算法,利用具有关联性的参数构建数据向量,通过聚类分析自动建立健康数据向量的族类阈值区间。关联关系的破坏将引起部分参数超出族类阈值区间,使系统的异常程度存在模糊性与随机性。引入云模型评价指标,将闭环系统异常程度的不确定性通过熵与超熵定量表示,从而更加准确地判断闭环系统的异常程度。仿真结果表明:该方法能够建立卫星飞轮闭环系统的族类知识库,并可以根据云模型提供的定性知识有效判断系统的异常程度。

Abnormality detection for flywheels based on data association analysis

In order to solve the problem that the early faults hardly to be discovered in the spacecraft under closed-loop system and the precise mathematical model barely to be established, inductive monitoring system (IMS) based on data association analysis is proposed to detect the abnormality in closedloop system. An unsupervised learning clustering algorithm is employed and can automatically characterize the threshold interval of each cluster by analyzing the nominal system operation data vectors with parameter correlation. Some parameters in the vectors may overflow their corresponding cluster intervals because of correlation damages during the abnormal operations. And there are fuzziness and randomness in measuring the degree of abnormality in the system. By introducing the cloud model index,the backward cloud can quantify the uncertainty of abnormal degrees in the closedloop system by the entropy and the hyper entropy indices, so that the abnormal degrees could be judgedmore accurately . Simulation results show that the method employed can build the cluster knowledge bases of satellite flywheels with closedloop systems, and the normal cloud model can offer the qualitative knowledge about damages in the simulink model. effectively judge the degree of system abnormality according to the qualitative knowledge of the normal cloud model.