Random forests classifier for machine fault diagnosis |
| |
Authors: | Bo-Suk Yang Xiao Di Tian Han |
| |
Affiliation: | (1) School of Mechanical Engineering, Pukyong National University, San 100, Yongdang-dong, Nam-gu, Busan, 608-739, South Korea;(2) School of Mechanical Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, 100083 Beijing, China |
| |
Abstract: | This paper investigates the possibilities of applying the random forests algorithm (RF) in machine fault diagnosis, and proposes
a hybrid method combined with genetic algorithm to improve the classification accuracy. The proposed method is based on RF,
a novel ensemble classifier which builds a number of decision trees to improve the single tree classifier. Although there
are several existing techniques for faults diagnosis, the application research on RF is meaningful and necessary because of
its fast execution speed, the characteristics of tree classifier, and high performance in machine faults diagnosis. The proposed
method is demonstrated by a case study on induction motor fault diagnosis. Experimental results indicate the validity and
reliability of RF-based diagnosis method. |
| |
Keywords: | Random forests algorithm Genetic algorithm Machine learning Fault diagnosis Rotating machinery |
本文献已被 SpringerLink 等数据库收录! |
|