NDLI: Random forests classifier for machine fault diagnosis

Content Provider	SpringerLink
Author	Yang, Bo Suk Di, Xiao Han, Tian
Copyright Year	2008
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.
Starting Page	1716
Ending Page	1725
Page Count	10
File Format	PDF
ISSN	1738494X
Journal	Journal of Mechanical Science and Technology
Volume Number	22
Issue Number	9
e-ISSN	19763824
Language	English
Publisher	Korean Society of Mechanical Engineers
Publisher Date	2009-01-23
Publisher Place	Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Random forests algorithm Genetic algorithm Machine learning Fault diagnosis Rotating machinery Industrial and Production Engineering Vibration, Dynamical Systems, Control Mechanical Engineering
Content Type	Text
Resource Type	Article
Subject	Mechanics of Materials Mechanical Engineering

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