A Method for Straw Crushing Tool Wear State Recognition Based on HFS and Ensemble Learning Model

• Published in: International journal of precision engineering and manufacturing Vol. 26; no. 8; pp. 1907 - 1920
• Main Authors: Zhou, Long, Xie, Lirong, Bian, Yifan, Lin, Zhikang, Shi, Minglei
• Format: Journal Article
• Language: English
• Published: Seoul Korean Society for Precision Engineering 01.08.2025; Springer Nature B.V
• Subjects: Accuracy; Crushing; Engineering; Ensemble learning; Industrial and Production Engineering; Machine learning; Materials Science; Newton-Raphson method; Noise reduction; Redundancy; Regular Paper; Support vector machines; Tool wear; INRBO-XGBoost; HFS; Multi-domain feature; Signal cancellation; Straw crushing tool

Aiming at the problem of low recognition accuracy due to high feature dimensionality, redundancy and inconspicuous key features in the process of straw crushing tool wear state recognition, the study designed a hybrid feature selection (HFS) with maximum mutual information coefficient (MIC)combined...