Abstract
As the number of satellite launches increases each year, it is only natural that an interest in the safety and monitoring of these systems would increase as well. However, as a system becomes more complex, generating a high-fidelity model that accurately describes the system becomes complicated. Therefore, imploring a data-driven method can provide to be more beneficial for such applications. This research proposes a novel approach for data-driven machine learning techniques on the detection and isolation of nonlinear systems, with a case-study for an in-orbit closed loop-controlled satellite with reaction wheels as actuators. High-fidelity models of the 3-axis controlled satellite are employed to generate data for both nominal and faulty conditions of the reaction wheels. The generated simulation data is used as input for the isolation method, after which the data is pre-processed through feature extraction from a temporal, statistical, and spectral domain. The pre-processed features are then fed into various machine learning classifiers. Isolation results are validated with cross-validation, and model parameters are tuned using hyperparameter optimization. To validate the robustness of the proposed method, it is tested on three characterized datasets and three reaction wheel configurations, including standard four-wheel, three-orthogonal, and pyramid. The results prove superior performance isolation accuracy for the system under study compared to previous studies using alternative methods (Rahimi & Saadat, 2019, 2020).
Highlights
With the ever-growing number of satellites being launched into space, it is crucial that the health monitoring and safety of these systems be advanced enough to compensate for the lack of redundant components and their decreasing size
For the cross-validation, the random forest classifier was employed to save on computational costs, the splitting strategy was set to 10-folds, and the parameter that was varied in this analysis was the max depth of the trees
The performance of the proposed method was evaluated for a case study involving fault detection and isolation (FDI) of an ADCS system using reaction wheels (RWs) as actuators onboard an in-orbit satellite
Summary
With the ever-growing number of satellites being launched into space, it is crucial that the health monitoring and safety of these systems be advanced enough to compensate for the lack of redundant components and their decreasing size. Smaller satellites require less cost for design and mass-production, meaning multiple can be launched into space at a time. To ensure reliability and mission success, the health and maintenance of these systems are essential. Fault detection and isolation (FDI) methods for ADCS has developed great incentive to be improved and advanced
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