基于双模型自适应估计的大气数据传感器故障诊断方法

doi:10.12305/j.issn.1001-506X.2024.11.21

Abstract

Abstract:

Atmospheric turbulence is an irregular random motion in the atmosphere. During the measurement of airborne air data sensors, atmospheric turbulence effects and sensor faults are coupled with each other, leading to the inability of air data sensor fault diagnosis algorithms to decouple faults from turbulence effects. In view of the fault diagnosis problem of air data sensors under the effects of atmospheric turbulence, a new atmospheric system model and measurement model are developed based on inertial measurement units and navigation attitude solving, considering the effects of turbulence on the atmospheric system. After extending the double-model adaptive estimation algorithm, the covariance adaptive update of the effect of turbulence on the system is introduced to achieve an unbiased estimation of the fault state in the presence of unknown disturbances. Simulation results show that the algorithm can effectively achieve diagnosis of fixed deviation faults, drift deviation faults, and oscillation faults.

Key words: air data sensor fault diagnosis, atmospheric turbulence, double-model adaptive estimation, fault detection and diagnosis

CLC Number:

V241.6

Yingfei XIAO, Haiying LIU, Yuehua CHENG, Tiexiang LI. Fault diagnosis method of air data sensor based on double-model adaptive estimation[J]. Systems Engineering and Electronics, 2024, 46(11): 3792-3799.

Figures/Tables 7

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References 30

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Fault diagnosis method of air data sensor based on double-model adaptive estimation

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