MATEC Web Conf.
Volume 77, 20162016 3rd International Conference on Mechanics and Mechatronics Research (ICMMR 2016)
|Number of page(s)||5|
|Section||Design and Study on Machinery|
|Published online||03 October 2016|
Fault Identification Method of Ball Bearing Based on IAs and SVMs
School of Mechanic Technology and Engineering, Jilin University, Changchun, 130025, China
In order to effectively identify the bearing running condition, this paper proposed a new method which combines local mean decomposition (LMD) and support vector machine (SVM) together for ball bearing fault identification. Firstly, the gathered vibration signals were decomposed into a number of product functions (PFs) by LMD, with each PF corresponding to an instantaneous amplitude (IA) signal and instantaneous frequency (IF) signal. Then, introduce the concept of fault characteristic amplitude ratios which can be used to construct fault feature vectors; the extracted characteristic features were input into SVM to train and construct the fault identification model; the bearing running state identification was thereby realized. Cases of normal and fault were analyzed. Experimental results show that the proposed algorithm can diagnose the bearing failures reasonable and efficient.
© The Authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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