@inproceedings{c356a0692bb34f38873df30c1c06001d,
title = "Gear fault diagnosis based on SVM",
abstract = "Elements of Support Vector Machine was applied to the fault diagnosis of gear system, and the two-class algorithms for 3 individual fault modes, which are No Fault Gear Mode, Crack of Dedendum Mode and Tooth Surface Abrasion Mode respectively, are well developed and set up. Through the training and testing simulation data samples and the signal samples from gear oscillation, these 3 different types of gear fault modes are finally identified and distinguished from each other at the rotating speed of 300r/min and 900r/min. The result validates that the Support Vector Machine is with excellent diagnostic ability in the fault diagnosis system of gear and with favorable prospect in this filed of application.",
keywords = "Fault diagnosis, Feature extraction, Gear system, Support vector machine",
author = "Ma, {Shang Jun} and Geng Liu and Yongqiang Xu",
year = "2010",
doi = "10.1109/ICWAPR.2010.5576299",
language = "英语",
isbn = "9781424465309",
series = "2010 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2010",
pages = "140--143",
booktitle = "2010 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2010",
note = "2010 8th International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2010 ; Conference date: 11-07-2010 Through 14-07-2010",
}