Hydraulic Systems Fault Diagnosis Based on Random Forests Recursive Feature Elimination and XGBoost

Abstract

This paper proposes an RFRFE-XGBoost method for fault mode recognition in hydraulic systems. The proposed method combines random forests-based recursive feature elimination (RFRFE) and extreme gradient boosting (XGBoost) to effectively identify important features and improve fault diagnosis efficiency and accuracy. The method is validated on relevant datasets and compared with existing methods, demonstrating its effectiveness and superiority. The results show that RFRFE-XGBoost can accurately recognize various fault modes and outperforms other methods in terms of classification accuracy and computational efficiency. The proposed method provides a promising approach for fault diagnosis in complex systems.