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基于分層神經(jīng)網(wǎng)絡(luò)的航天器故障診斷技術(shù)

Spacecraft fault diagnosis based on hierarchical neural network

  • 摘要: 為了提高衛(wèi)星、飛船等復(fù)雜系統(tǒng)的故障診斷速度和精度，文章提出了一種基于分層神經(jīng)網(wǎng)絡(luò)的整星故障診斷模型。模型中的上層神經(jīng)網(wǎng)絡(luò)采用自組織特征映射網(wǎng)絡(luò)，完成整星故障的初步定位與辨識；下層神經(jīng)網(wǎng)絡(luò)采用廣義回歸神經(jīng)網(wǎng)絡(luò)，實現(xiàn)整星各分系統(tǒng)故障的精確定位和定因。引入主元分析法實現(xiàn)原始狀態(tài)變量的降維，減少神經(jīng)網(wǎng)絡(luò)神經(jīng)元數(shù)量。該模型已成功應(yīng)用于某衛(wèi)星各分系統(tǒng)的故障診斷，提高了診斷效率，并能精確給出診斷結(jié)果。

     

    Abstract: For improving the diagnosis speed and accuracy of a large-scale and complex system like satellite or spaceship, a hierarchical diagnosis model of satellite is proposed. A self organizing feature mapping neural network is adopted for the upper network, which is responsible for the preliminary fault localization and identification for the whole satellite. Generalized regression neural network(GRNN) is adopted for the lower network, which is responsible for accurately determining the localization and causes of faults for each subsystem of the satellite. The principal component analysis(PCA) is introduced to reduce the dimension of the original state variables. So, the number of the upper neural network neurons is reduced. The method is successfully applied to the fault diagnosis for the subsystems of a satellite. The accurate diagnosis result is obtained with improved efficiency.

     

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