DocumentCode :
595188
Title :
Semi-supervised adaptive parzen Gentleboost algorithm for fault diagnosis
Author :
Chengliang Li ; Zhongsheng Wang ; Shuhui Bu ; Zhenbao Liu
Author_Institution :
Sch. of Aeronaut., Northwestern Polytech. Univ., Xi´´an, China
fYear :
2012
fDate :
11-15 Nov. 2012
Firstpage :
2290
Lastpage :
2293
Abstract :
In this paper, we present a novel semi-supervised strategy for machine fault diagnosis. In the proposed method, we select parzen window as the generative classifier and Gentleboost as the discriminative classifier. Compared with SVM, boosting method has a very interesting property of relative immunity to overfitting. In addition, we propose a novel adaptive parzen window algorithm. It employs variational adaptive parzen window rather than a global optimized and fixed window, therefore, more accurate density estimates can be obtained. In experiments, artificial and machine vibration data are used to compare with other algorithms. Our proposed algorithm achieves stronger robustness and lower classification error rate.
Keywords :
fault diagnosis; mechanical engineering computing; pattern classification; probability; support vector machines; adaptive parzen window algorithm; boosting method; density estimation; discriminative classifier; generative classifier; machine fault diagnosis; semi-supervised adaptive parzen Gentleboost algorithm; variational adaptive parzen window algorithm; Adaptation models; Bandwidth; Classification algorithms; Error analysis; Fault diagnosis; Support vector machines; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ICPR), 2012 21st International Conference on
Conference_Location :
Tsukuba
ISSN :
1051-4651
Print_ISBN :
978-1-4673-2216-4
Type :
conf
Filename :
6460622
Link To Document :
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