Abstract
The health status of the momentum wheel is vital for a satellite. Recently, research on anomaly detection for satellites has become more and more extensive. Previous research mostly required simulation models for key components. However, the physical models are difficult to construct, and the simulation data does not match the telemetry data in engineering applications. To overcome the above problem, this paper proposes a new anomaly detection framework based on real telemetry data. First, the time-domain and frequency-domain features of the preprocessed telemetry signal are calculated, and the effective features are selected through evaluation. Second, a new Huffman-multi-scale entropy (HMSE) system is proposed, which can effectively improve the discrimination between different data types. Third, this paper adopts a multi-class SVM model based on the directed acyclic graph (DAG) principle and proposes an improved adaptive particle swarm optimization (APSO) method to train the SVM model. The proposed method is applied to anomaly detection for satellite momentum wheel voltage telemetry data. The recognition accuracy and detection rate of the method proposed in this paper can reach 99.60% and 99.87%. Compared with other methods, the proposed method can effectively improve the recognition accuracy and detection rate, and it can also effectively reduce the false alarm rate and the missed alarm rate.
Highlights
As important spacecraft, study of the reliability of artificial satellites is a hot topic at present
Both genetic algorithm (GA) and Particle swarm optimization (PSO) can solve high-dimensional complex optimization problems well, in the iterative process of PSO, the particles can retain the memory of the good solution, but the GA cannot, so PSO can often converge to a better solution more quickly
We propose a new detection framework for anomaly detection based on spacecraft telemetry data
Summary
Study of the reliability of artificial satellites is a hot topic at present. In terms of constructing a new kernel function, Wang et al proposed a kernel function selection mechanism under sparse representation and the superiority of the selection mechanism was performed in simulations and engineering experiments involving high-speed bearing fault diagnosis [27] Both GA and PSO can solve high-dimensional complex optimization problems well, in the iterative process of PSO, the particles can retain the memory of the good solution, but the GA cannot, so PSO can often converge to a better solution more quickly. In response to the above problems, this article proposes a new method based on multi-type features fusion and improved SVM to handle the problem of anomaly detection for the satellite.
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