A Method for Detecting Incipient Faults in Satellites Based on Dynamic Linear Discriminant Analysis

Abstract

Timely detection and treatment of possible incipient faults in satellites will effectively reduce the damage and harm they could cause. Although much work has been done concerning fault detection problems, the related questions about satellite incipient faults are little addressed. In this paper, a new satellite incipient fault detection method was proposed by combining the ideas of deviation in unsupervised fault detection methods and classification in supervised fault detection methods. First, the proposed method uses dynamic linear discriminant analysis (LDA) to find an optimal projection vector that separates the in-orbit data from the normal historical data as much as possible. Second, under the assumption that the parameters obey a multidimensional Gaussian distribution, it applies the normal historical data and the optimal projection vector to build a normal model. Finally, it employs the noncentral F-distribution to test whether a fault has occurred. The proposed method was validated using a numerical simulation case and a real satellite fault case. The results show that the method proposed in this paper is more effective at detecting incipient faults than traditional methods.

Fault Diagnosis and Prognosis for Satellite Formation Flying: A Survey

“…Besides, several fault diagnosis methods are applied to time-invariant or static topologies. It seems more realistic to apply it to a dynamic or time-varying communication graph [58]. In addition, additive faults are usually considered in the systems in the reviewed literature.…”

“…By incorporating unsupervised and supervised machine learning techniques, a new method for fault detection has been developed by Zhang et al [58]. In the first stage, a fault detection method is offered by applying linear discriminant analysis (LDA) to collect historical data as healthy data and satellite in-orbit data as faulty data and then discriminate them from each other by finding the best projection vector.…”

“…Finally, satellite in-orbit faulty data can be detected by recognizing any significant deviation from the normal data of the standard model. The shortcomings of [58] are that LDA is sensitive to nonlinearity, and nongaussian distributions can undermine its efficacy.…”