@inproceedings{f81d3b9f200c4ff7b807b3d06ad52c2f,
title = "Rolling fault diagnosis via robust semi-supervised model with capped l2,1-norm regularization",
abstract = "Rolling element bearings play an important role in ensuring the availability of industrial machines. Unexpected bearing failures in such machines during field operation can lead to machine breakdown, which may have some pretty severe implications. However, the insufficiency of labeled samples is major problem for handling fault diagnosis problem. To address such concern, we propose a semi-supervised method for diagnosing faulty bearings by utilizing unlabeled samples. The superiority of our algorithm has been validated by comparison with other state-of art methods based on a rolling element bearing data. The two-dimensional visualization and classification accuracy of bearing data show that our algorithm is able to recognize different bearing fault categories effectively. Thus, it can be considered as a promising method for fault diagnosis.",
keywords = "Capped ℓ-Norm, Fault Diagnosis, Semi-supervised Learning",
author = "Mingbo Zhao and Chow, \{Tommy W.S.\} and Haijun Zhang and Yan Li",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Industrial Technology, ICIT 2017 ; Conference date: 23-03-2017 Through 25-03-2017",
year = "2017",
month = apr,
day = "26",
doi = "10.1109/ICIT.2017.7915509",
language = "英语",
series = "Proceedings of the IEEE International Conference on Industrial Technology",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1064--1069",
booktitle = "2017 IEEE International Conference on Industrial Technology, ICIT 2017",
address = "美国",
}