NDLI: Fault diagnosis of rolling bearings using multifractal detrended fluctuation analysis and Mahalanobis distance criterion

Content Provider	IEEE Xplore Digital Library
Author	Lin, J. Chen, Q. Tian, X. Gu, F.
Copyright Year	2012
Description	Author affiliation: State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics Nanjing, P. R. China (Lin, J.; Chen, Q.) \|\| Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield, U.K. (Tian, X.; Gu, F.)
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 the 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.
Starting Page	1
Ending Page	6
File Size	254200
Page Count	6
File Format	PDF
ISBN	9781467317221
e-ISBN	9781908549006
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-07
Publisher Place	United Kingdom
Access Restriction	Subscribed
Rights Holder	CACSUK
Subject Keyword	Vibrations Fault diagnosis Detrended fluctuation analysis Fluctuations Time series analysis Rolling bearings Feature extraction Fractals Mahalanobis distance Rolling bearing Multifractal
Content Type	Text
Resource Type	Article

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