Abnormality detection for flywheels based on data association analysis

被引：3

作者：

Gong, Xuebing ^{[1
]}

Wang, Rixin ^{[1
]}

Xu, Minqiang ^{[1
]}

机构：

[1] Deep Space Exploration Research Center, Harbin Institute of Technology, Harbin

来源：

Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica | 2015年 / 36卷 / 03期

关键词：

Abnormality detection; Association analysis; Closed loop systems; Cloud model; Flywheels; Unsupervised learning;

D O I：

10.7527/S1000-6893.2014.0124

中图分类号：

学科分类号：

摘要：

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. ©, 2015, AAAS Press of Chinese Society of Aeronautics and Astronautics. All right reserved.

引用

页码：898 / 906

页数：8

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