Shock and Vibration

Shock and Vibration / 2012 / Article

Open Access

Volume 19 |Article ID 438789 |

Pei-Lin Zhang, Bing Li, Shuang-Shan Mi, Ying-Tang Zhang, Dong-Sheng Liu, "Bearing Fault Detection Using Multi-Scale Fractal Dimensions Based on Morphological Covers", Shock and Vibration, vol. 19, Article ID 438789, 11 pages, 2012.

Bearing Fault Detection Using Multi-Scale Fractal Dimensions Based on Morphological Covers

Received04 Nov 2010
Revised29 Oct 2011


Vibration signals acquired from bearing have been found to demonstrate complicated nonlinear characteristics in literature. Fractal geometry theory has provided effective tools such as fractal dimension for characterizing the vibration signals in bearing faults detection. However, most of the natural signals are not critical self-similar fractals; the assumption of a constant fractal dimension at all scales may not be true. Motivated by this fact, this work explores the application of the multi-scale fractal dimensions (MFDs) based on morphological cover (MC) technique for bearing fault diagnosis. Vibration signals from bearing with seven different states under four operations conditions are collected to validate the presented MFDs based on MC technique. Experimental results reveal that the vibration signals acquired from bearing are not critical self-similar fractals. The MFDs can provide more discriminative information about the signals than the single global fractal dimension. Furthermore, three classifiers are employed to evaluate and compare the classification performance of the MFDs with other feature extraction methods. Experimental results demonstrate the MFDs to be a desirable approach to improve the performance of bearing fault diagnosis.

Copyright © 2012 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.

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