Hybrid Approach of XGBoost and Rule-based Model for Fault Detection and Severity Estimation in Spacecraft Propulsion System

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Published Sep 4, 2023
Sang Kyung Lee Jiwon Lee Seungyun Lee Bongmo Kim Yong Chae Kim Jinwook Lee Byeng Dong Youn

Abstract

This study presents a method for fault detection and severity estimation of the spacecraft propulsion system. The spacecraft propulsion system is complicated, consisting of many valves, and operates in a harsh environment. Therefore, faults due to external factors such as bubbles or valve breakage can occur within the complex system at any time. To diagnose faults in this system, we propose a hybrid method of XGBoost-based method and rule-based method. In the XGBoost-based method, the overall fault classification, including unknown fault filtering was performed. In addition, the rule-based model was performed to estimate the fault severity. The results show that the proposed method reached a 99.94% score, which is calculated by the score matrix considering fault classification accuracy and severity estimation.  

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Keywords

xgboost, fault detection, severity estimation, spacecraft propulsion system, tsfresh

References
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Section
Data Challenge Papers