@inproceedings{f6ad49b2c9914c09b096f8a7bae89959,
title = "A novel fault diagnosis method based on time-frequency image recognition",
abstract = "A novel intelligent fault diagnosis method based on vibration time-frequency image recognition is proposed in this paper. First, Smooth pseudo Wigner-Ville distribution (SPWVD) is employed to represent the time-frequency distribution characteristics. Then, the features of time-frequency images are extracted by using locality-constrained linear coding (LLC) and spatial pyramid matching. Next, we use the support vector machine to identify these feature vectors for realizing intelligent fault detection. The promise of our algorithm is illustrated by performing above procedures on the vibration signals measured from rolling element bearing with sixteen operating states. Experimental results show that the proposed method can acquire higher diagnosis accuracy compared with the ScSPM method in rolling element bearing diagnosis.",
keywords = "Intelligent fault diagnosis, Locality-constrained linear coding, Rolling element bearing, Time-frequency image",
author = "Wang, \{Wei Gang\} and Liu, \{Zhan Sheng\}",
note = "Publisher Copyright: {\textcopyright} (2014) Trans Tech Publications, Switzerland.; 2014 International Conference on Manufacturing Technology and Electronics Applications, ICMTEA 2014 ; Conference date: 08-11-2014 Through 09-11-2014",
year = "2014",
doi = "10.4028/www.scientific.net/AMM.687-691.3569",
language = "英语",
series = "Applied Mechanics and Materials",
publisher = "Trans Tech Publications Ltd",
pages = "3569--3573",
editor = "Zhang Lin and Hongying Hu and Yajun Zhang and Jianguo Qiao and Jiamin Xu",
booktitle = "Manufacturing Technology, Electronics, Computer and Information Technology Applications",
address = "瑞士",
}