Feature-weighted Random Forest with Boruta for Fault Diagnosis of Satellite Attitude Control Systems

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Published Nov 5, 2024
Shaozhi Chen Xiaopeng Xi Maiying Zhong Marcos E. Orchard

Abstract

The performance of random forest (RF) based satellite attitude control system (ACS) fault diagnosis methods is limited by uninformative features in high-dimensional data. To solve this problem, we proposed a feature-weighted random forest with Boruta (FWRFB) based fault diagnosis method is proposed for fault diagnosis of ACSs. Firstly, a Boruta feature selection algorithm is used to obtain a feature set and determine significant feature weights. Subsequently, a novel feature-weighted random forest (FWRF) algorithm is designed, which utilizes feature-weighted random sampling instead of simple random sampling to generate feature subsets in the RF. The FWRFB effectively utilizes the feature information while mitigating noise interference. Finally, a FWRFB-based diagnostic module is developed for online fault diagnosis of ACSs. The effectiveness of the proposed method is verified by the ACS data from a semi-physical simulation platform.

How to Cite

Chen, S., Xi, X., Zhong, M., & Orchard, M. E. (2024). Feature-weighted Random Forest with Boruta for Fault Diagnosis of Satellite Attitude Control Systems. Annual Conference of the PHM Society, 16(1). https://doi.org/10.36001/phmconf.2024.v16i1.4132
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Keywords

Feature-weighted random forest, Fault diagnosis, Satellite attitude control system, Boruta

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Section
Technical Research Papers

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