Identification of feature set for effective tool condition monitoring by acoustic emission sensing

被引:42
作者
Sun, J [1 ]
Hong, GS [1 ]
Rahman, M [1 ]
Wong, YS [1 ]
机构
[1] Natl Univ Singapore, Dept Mech Engn, Singapore 119260, Singapore
关键词
D O I
10.1080/00207540310001626652
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In tool condition monitoring systems, various features from suitably processed acoustic emission signals are utilized by researchers. However, not all of these features are equally informative in a specific monitoring system: certain features may correspond to noise, not information; others may be correlated or not relevant for the task to be realized. This study comprehensively takes all these known signal features and aims to identify the most effective set that can give robust and reliable identification of tool condition. In this paper, the aim is investigated through feature selection, in which automatic relevance determination (ARD) under a Bayesian framework and support vector machine (SVM) are coupled together to perform this task. In tool condition monitoring, this proposed method is able to identify the worst features according to their corresponding ARD parameters and delete them. Then the effectiveness of this pruning may be evaluated by a model validation. Finally, the effective feature set in the developed tool wear recognition system is obtained. The experimental results show that the AE feature set selected through this method is more effective and efficient to recognize tool status over various cutting conditions.
引用
收藏
页码:901 / 918
页数:18
相关论文
共 37 条
[1]  
Bishop C. M., 1996, Neural networks for pattern recognition
[2]  
BLUM T, 1990, J ENG IND-T ASME, V112, P203, DOI 10.1115/1.2899576
[3]  
CAO LJ, 2001, THESIS NATL U SINGAP
[4]  
CHENOWETH T, 1995, NEUROVEST J, V3, P14
[5]   Real-time monitoring of tool fracture in turning using sensor fusion [J].
Choi, D ;
Kwon, WT ;
Chu, CN .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 1999, 15 (05) :305-310
[6]   Bayesian trigonometric support vector classifier [J].
Chu, W ;
Keerthi, SS ;
Ong, CJ .
NEURAL COMPUTATION, 2003, 15 (09) :2227-2254
[7]   APPLICATION OF ACOUSTIC-EMISSION TECHNIQUES IN MANUFACTURING [J].
DORNFELD, D .
NDT & E INTERNATIONAL, 1992, 25 (06) :259-269
[8]  
DU R, 1995, J ENG IND-T ASME, V117, P121, DOI 10.1115/1.2803286
[9]  
EMEL E, 1988, ASME, V110, P137
[10]  
IWATA K, 1977, ANN CIRP, V26, P19