Bearing Health Assessment Based on Chaotic CharacteristicsReport as inadecuate

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Shock and Vibration - Volume 20 2013, Issue 3, Pages 519-530

School of Reliability and Systems Engineering, Beihang University, Beijing, China

Science and Technology Laboratory on Reliability and Environmental Engineering, Beijing, China

Received 20 October 2012; Accepted 7 December 2012

Copyright © 2013 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Vibration signals extracted from rotating parts of machinery carry a lot of useful information about the condition of operating machine. Due to the strong non-linear, complex and non-stationary characteristics of vibration signals from working bearings, an accurate and reliable health assessment method for bearing is necessary. This paper proposes to utilize the selected chaotic characteristics of vibration signal for health assessment of a bearing by using self-organizing map SOM. Both Grassberger-Procaccia algorithm and Takens- theory are employed to calculate the characteristic vector which includes three chaotic characteristics, such as correlation dimension, largest Lyapunov exponent and Kolmogorov entropy. After that, SOM is used to map the three corresponding characteristics into a confidence value CV which represents the health state of the bearing. Finally, a case study based on vibration datasets of a group of testing bearings was conducted to demonstrate that the proposed method can reliably assess the health state of bearing.

Author: Chen Lu, Qian Sun, Laifa Tao, Hongmei Liu, and Chuan Lu



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