Feature representation learning by knowledge informed evolutionary generating programming for remaining useful life prediction of winders
TL;DR
Experimental results demonstrate that the proposed method significantly improves fault prediction accuracy and stability compared to traditional techniques and standard GEP approaches, demonstrating its practical value for industrial applications.
Abstract
Feature extraction from high-speed winding machines is crucial in diagnosing equipment failures in chemical fiber production. However, current methods often face challenges in prediction accuracy and adaptability, especially when accounting for varying degradation modes and operational conditions. This paper proposes a data-knowledge fusion evolutionary generating programming method for feature representation of winders. Firstly, the operational data of winders is preprocessed. Subsequently, the adaptability of these features is improved through knowledge informed feature extraction and correlation analysis. Finally, an evolutionary generating method integrates these enhancements with data-knowledge fusion, resulting in the gene expression programming with correlation analysis fault feature set (GEP-CA), which facilitates adaptive remaining useful life (RUL) prediction for winders. The proposed method is validated using both the PHM2012 challenge bearing dataset and real-world data from high-speed winding machines. Experimental results demonstrate that the proposed method significantly improves fault prediction accuracy and stability compared to traditional techniques and standard GEP approaches, demonstrating its practical value for industrial applications.