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Fault Diagnosis of Rolling Bearings using Multifractal Detrended Fluctuation Analysis and Mahalanobis Distance Criterion

Lin, Jinshan, Chen, Qian, Tian, Xiange and Gu, Fengshou (2012) Fault Diagnosis of Rolling Bearings using Multifractal Detrended Fluctuation Analysis and Mahalanobis Distance Criterion. In: Proceedings of the 18th International Conference on Automation and Computing (ICAC) 2012: Integration of Design and Engineering. IEEE, Loughborough, UK, pp. 1-6. ISBN 9781467317221

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Abstract

Vibrations of a defective rolling bearing often
exhibit nonstationary and nonlinear characteristics which are
submerged in strong noise and interference components. Thus,
diagnostic feature extraction is always a challenge and has
aroused wide concerns for a long time. In this paper, the
multifractal detrended fluctuation analysis (MF-DFA) is
applied to uncover the multifractality buried in nonstationary
time series for exploring rolling bearing fault data.
Subsequently, a new approach for fault diagnosis is proposed
based on MF-DFA and Mahalanobis distance criterion. The
multifractality of bearing data is estimated with the
generalized Hurst exponent and the multifractal spectrum.
Five characteristic parameters which are sensitive to changes
of bearing fault conditions are extracted from the spectrum
for diagnosis of fault sizes. For benchmarking this new
method, the empirical mode decomposition (EMD) method is
also employed to analyze the same dataset. The results show
that MF-DFA outperforms EMD in revealing the nature of
rolling bearing fault data.

Item Type: Book Chapter
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Schools: School of Computing and Engineering
School of Computing and Engineering > Automotive Engineering Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre > Energy, Emissions and the Environment Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre > Machinery Condition and Performance Monitoring Research Group
School of Computing and Engineering > High-Performance Intelligent Computing > Information and Systems Engineering Group
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Depositing User: Sara Taylor
Date Deposited: 20 Sep 2012 13:03
Last Modified: 05 Jun 2013 11:50
URI: http://eprints.hud.ac.uk/id/eprint/14998

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