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

J. Lin, Q. Chen, X. Tian, F. Gu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publication18th International Conference on Automation and Computing, ICAC 2012
PublisherIEEE
Pages152-157
Number of pages6
ISBN (Electronic)9781908549006
ISBN (Print)9781467317221
Publication statusPublished - 15 Oct 2012
Event18th International Conference on Automation and Computing: Integration of Design and Engineering - Loughborough University, Leicestershire, United Kingdom
Duration: 7 Sep 20128 Sep 2012
Conference number: 18
https://www.ieee.org/conferences_events/conferences/conferencedetails/index.html?Conf_ID=20732 (Link to Conference Website)

Conference

Conference18th International Conference on Automation and Computing
Abbreviated titleICAC 2012
CountryUnited Kingdom
CityLeicestershire
Period7/09/128/09/12
Internet address

Fingerprint

Bearings (structural)
Failure analysis
Decomposition
Benchmarking
Feature extraction
Time series

Cite this

Lin, J., Chen, Q., Tian, X., & Gu, F. (2012). Fault diagnosis of rolling bearings using multifractal detrended fluctuation analysis and Mahalanobis distance criterion. In 18th International Conference on Automation and Computing, ICAC 2012 (pp. 152-157). [6330497] IEEE.
Lin, J. ; Chen, Q. ; Tian, X. ; Gu, F. / Fault diagnosis of rolling bearings using multifractal detrended fluctuation analysis and Mahalanobis distance criterion. 18th International Conference on Automation and Computing, ICAC 2012. IEEE, 2012. pp. 152-157
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title = "Fault diagnosis of rolling bearings using multifractal detrended fluctuation analysis and Mahalanobis distance criterion",
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.",
keywords = "detrended fluctuation analysis, fault diagnosis, Mahalanobis distance, multifractal, rolling bearing",
author = "J. Lin and Q. Chen and X. Tian and F. Gu",
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Lin, J, Chen, Q, Tian, X & Gu, F 2012, Fault diagnosis of rolling bearings using multifractal detrended fluctuation analysis and Mahalanobis distance criterion. in 18th International Conference on Automation and Computing, ICAC 2012., 6330497, IEEE, pp. 152-157, 18th International Conference on Automation and Computing, Leicestershire, United Kingdom, 7/09/12.

Fault diagnosis of rolling bearings using multifractal detrended fluctuation analysis and Mahalanobis distance criterion. / Lin, J.; Chen, Q.; Tian, X.; Gu, F.

18th International Conference on Automation and Computing, ICAC 2012. IEEE, 2012. p. 152-157 6330497.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Fault diagnosis of rolling bearings using multifractal detrended fluctuation analysis and Mahalanobis distance criterion

AU - Lin, J.

AU - Chen, Q.

AU - Tian, X.

AU - Gu, F.

PY - 2012/10/15

Y1 - 2012/10/15

N2 - 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.

AB - 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.

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KW - fault diagnosis

KW - Mahalanobis distance

KW - multifractal

KW - rolling bearing

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Lin J, Chen Q, Tian X, Gu F. Fault diagnosis of rolling bearings using multifractal detrended fluctuation analysis and Mahalanobis distance criterion. In 18th International Conference on Automation and Computing, ICAC 2012. IEEE. 2012. p. 152-157. 6330497