A New Method for Satellite Control System Fault Pattern Recognition combining Multi-Classification SVM with Kernel Principal Component Analysis

Xia, Keqiang; Xing, Meng; Wei, Jun; Lan, Baojun; Zhuo, Chen; Li, Ganhua

doi:10.1109/itnec.2019.8729412

Cited by 7 publications

(2 citation statements)

References 3 publications

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“…based techniques such as (SVM with PCA) [24] that introduced accuracy up to 97.4% and ARMA Model-Based FDI technique in [15] produce detection and identification accuracy up to 96% and 98% respectively. Furthermore, regarding their algorithm complexity and number of features employed, the high orders of these techniques' required more processing time than other suggested techniques.…”

Section: Results Analysis Open Challenges and Promising Directionmentioning

confidence: 99%

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Spacecraft fault detection and identification techniques using artificial intelligence

Abdel Aziz,

Salama,

Mohamed

et al. 2023

J. Phys.: Conf. Ser.

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The complexity of spacecraft systems and their missions is increasing, requiring higher levels of performance and innovative solutions. It is essential to have onboard autonomy with minimal faults to ensure reliability, availability, and safety. Fault Detection and Identification (FDI) is critical in identifying spacecraft faults before they cause major failures. However, FDI design and application are challenging due to the space environment and the reliance on system information. To improve accuracy, speed, and noise robustness, modern FDI methods based on Artificial Intelligence (AI) techniques have been developed. This paper investigates the latest FDI techniques in the spacecraft attitude determination and control subsystem (ADCS) and electrical power subsystem (EPS). The article discusses various FDI methodologies and frameworks, highlighting their advantages, drawbacks, and the significance of AI implementation. Additionally, the paper presents a thorough analysis and comparison of the different methods.

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Section: Results Analysis Open Challenges and Promising Directionmentioning

confidence: 99%

“…A powerful technique for spacecraft ADCS has been proved using (SVM) combined with principal component analysis (PCA), which was introduced in [24]. Input data are converted to a low-dimensional feature vector using PCA to extract features.…”

Section: Hybrid Data-driven-based Fdi Techniquementioning

confidence: 99%