Abstract

Detection of faults at the incipient stage is critical to improving the availability and continuity of satellite services. The application of a local optimum projection vector and the Kullback–Leibler (KL) divergence can improve the detection rate of incipient faults. However, this suffers from the problem of high time complexity. We propose decomposing the KL divergence in the original optimization model and applying the property of the generalized Rayleigh quotient to reduce time complexity. Additionally, we establish two distribution models for subfunctions and to detect the slight anomalous behavior of the mean and covariance. The effectiveness of the proposed method was verified through a numerical simulation case and a real satellite fault case. The results demonstrate the advantages of low computational complexity and high sensitivity to incipient faults.

Highlights

  • IntroductionThe performance of these fault detection methods heavily relies on the experience of experts [18]

  • In traditional satellite fault detection methods, such as threshold-based methods [14,15] and model-based methods [16,17], the thresholds or the models required for fault detection necessitate manual setting

  • This paper proposes a new incipient fault detection method with lower computational complexity by decomposing the Kullback– Leibler (KL) divergence

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Summary

Introduction

The performance of these fault detection methods heavily relies on the experience of experts [18]. Data-driven fault detection methods have eliminated this heavy dependence on expert experience and become a popular research field [19,20,21,22]. These methods establish normal models based on satellite normal historical data, and compare the online data with the normal models to assess whether the online data is faulty. The methods proposed in the existing literature are mainly applied to serious faults, and an extremely small amount of research and application relates to incipient faults of satellites

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