A Review of Fault Detection and Diagnosis of Satellite Power Subsystem

Sun, Bo; Jin, Weihua; Liu, Zhidong; Zhang, Lei

doi:10.1007/978-3-030-69072-4_14

Cited by 1 publication

(2 citation statements)

References 18 publications

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“…Furthermore, regarding their algorithm complexity and number of features employed, the high orders of these techniques' required more processing time than other suggested techniques. Hybrid approaches that were presented in [23] based on (HVM-SVM) and different classifiers were compared as other technique performance. Using deeplearnning appear to have a major role in near future in design and implementation of FDI techniques results from OCSVM and LSTM in [10,22] introduced promising progress.…”

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%

“…In [23] a parameter reduction algorithm using hybrid voting mechanism (HVM) combined with SVM. (HVM-SVM) was used to improve SVM classification accuracy for spacecraft fault diagnosis.…”

Section: Hybrid Data-driven-based Fdi Techniquementioning

confidence: 99%