Semi-supervised adaptive parzen Gentleboost algorithm for fault diagnosis

Chengliang Li, Zhongsheng Wang, Shuhui Bu, Zhenbao Liu

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

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.

源语言英语
主期刊名ICPR 2012 - 21st International Conference on Pattern Recognition
2290-2293
页数4
出版状态已出版 - 2012
活动21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, 日本
期限: 11 11月 201215 11月 2012

出版系列

姓名Proceedings - International Conference on Pattern Recognition
ISSN(印刷版)1051-4651

会议

会议21st International Conference on Pattern Recognition, ICPR 2012
国家/地区日本
Tsukuba
时期11/11/1215/11/12

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