Authors
Weigang Wen, Weidong Cheng
Corresponding Author
Weigang Wen
Available Online 30 June 2014.
DOI
https://doi.org/10.2991/jrnal.2014.1.1.18
Keywords
Fault classification; Time delay; embedded dimension; phase space reconstruction; principal components analysis
Abstract
Rolling bearing is a common mechanical part which is subject to be damaged. It is important to monitor the condition of bearing. An effective mean is to extract faulty features of bearing from the vibration signal. In this paper, a method is introduced to realize intelligent classification of bearing state. The vibration signal is reconstructed into phase space by estimating the time delay and embedded dimension of time series. After reconstruction, fault classification is accomplished through normalized principal component analysis. It is testified that this method is effective for classifying fault of bearing by experiment and data analysis.

Copyright
© 2013, the Authors. Published by ALife Robotics Corp. Ltd..
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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