@inproceedings{e45b555ca082475fbe709203e87f7b85,
title = "Sparse subspace clustering for bearing fault classification",
abstract = "Bearing is a critical component that effects operational performance of machine. Fault classification to bearing that aims to identify category of bearing fault is helpful to improve reliability and safety of bearing. In this paper, a classification process is presented based on sparse subspace clustering. A sample corresponds to a specific fault state of the bearing is represented by its neighbourhood. Coefficient for data representation is solved by sparse representation. Spectral clustering is performed on the coefficient to classify the samples into its category. Effectiveness of the presented method is validated by test data of bearing with different degrees of fault. Comparison between sparse subspace clustering and other subspace analysis methods shows its effectiveness for classification further.",
keywords = "bearing, fault classification, sparse subspace",
author = "Chuang Sun and Bojian Wang and Shaohua Tian and Xuefeng Chen",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 10th International Conference on Sensing Technology, ICST 2016 ; Conference date: 11-11-2016 Through 13-11-2016",
year = "2016",
month = dec,
day = "22",
doi = "10.1109/ICSensT.2016.7796327",
language = "英语",
series = "Proceedings of the International Conference on Sensing Technology, ICST",
publisher = "IEEE Computer Society",
booktitle = "2016 10th International Conference on Sensing Technology, ICST 2016",
}