MATEC Web Conf.
Volume 148, 2018International Conference on Engineering Vibration (ICoEV 2017)
|Number of page(s)||5|
|Section||Vibration-Based Structural Health Monitoring Data Analysis and Time Series Methods|
|Published online||02 February 2018|
A new methodology for fault detection in rolling element bearings using singular spectrum analysis
Department of Mechanical engineering, the University of Wasit, Iraq
2 Department of Mechanical and Aerospace Engineering, the University of Strathclyde, Glasgow G1 1XJ
This paper proposes a vibration-based methodology for fault detection in rolling element bearings, which is based on pure data analysis via singular spectrum method. The method suggests building a baseline space from feature vectors made of the signals measured in the healthy/baseline bearing condition. The feature vectors are made using the Euclidean norms of the first three PC’s found for the signals measured. Then, the lagged version of any new signal corresponding to a new (possibly faulty) condition is projected onto this baseline feature space in order to assess its similarity to the baseline condition. The category of a new signal vector is determined based on the Mahalanobis distance (MD) of its feature vector to the baseline space. A validation of the methodology is suggested based on the results from an experimental test rig. The results obtained confirm the effective performance of the suggested methodology. It is made of simple steps and is easy to apply with a perspective to make it automatic and suitable for commercial applications.
© The Authors, published by EDP Sciences, 2018
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. (http://creativecommons.org/licenses/by/4.0/).
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