Research Papers

Rolling Bearing Localized Defect Evaluation by Multiscale Signature via Empirical Mode Decomposition

[+] Author and Article Information
Qingbo He

e-mail: qbhe@ustc.edu.cn

Fanrang Kong

e-mail: kongfr@ustc.edu.cnDepartment of Precision Machinery and Precision Instrumentation,
University of Science and
Technology of China,
Hefei, Anhui 230026,
People’s Republic of China

1Corresponding author.

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF VIBRATION AND ACOUSTICS. Manuscript received June 9, 2011; final manuscript received April 5, 2012; published online October 29, 2012. Assoc. Editor: Alan Palazzolo.

J. Vib. Acoust 134(6), 061013 (Oct 29, 2012) (11 pages) doi:10.1115/1.4006754 History: Received June 09, 2011; Revised April 05, 2012

Measured vibration signals from rolling element bearings with defects are generally nonstationary, and are multiscale in nature owing to contributions from events with different localization in time and frequency. This paper presents a novel approach to characterize the multiscale signature via empirical mode decomposition (EMD) for rolling bearing localized defect evaluation. Vibration signal measured from a rolling element bearing is first adaptively decomposed by the EMD to achieve a series of usable intrinsic mode functions (IMFs) carrying the bearing health information at multiple scales. Then the localized defect-induced IMF is selected from all the IMFs based on a variance regression approach. The multiscale signature, called multiscale slope feature, is finally estimated from the regression line fitted over logarithmic variances of the IMFs excluding the defect IMF. The presented feature reveals the pattern of energy transfer among multiple scales due to localized defects, representing an inherent self-similar signature of the bearing health information that is embedded on multiple analyzed scales. Experimental results verify the performance of the proposed multiscale feature, and further discussions are provided.

Copyright © 2012 by ASME
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Fig. 1

Procedure of the EMD with defect frequency detection

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Fig. 2

Demonstration of bearing signal decomposition by the EMD with defect frequency detection

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Fig. 3

Multiscale property of IMFs and WT coefficients of a Gaussian noise

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Fig. 4

Multiscale property of IMFs of a signal containing an interesting impulse component

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Fig. 5

The algorithm of multiscale slope feature extraction via the EMD

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Fig. 6

Four simulated signals: (a) waveform and (b) spectrum of X1(t), (c) waveform and (d) spectrum of X2(t), (e) waveform and (f) spectrum of X3(t), (g) waveform and (h) spectrum of X4(t)

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Fig. 7

Multiscale signature of IMFs with selection of interesting IMF for four simulated signals

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Fig. 8

Multiscale signature of DWT coefficients for four simulated signals

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Fig. 9

Experimental setup for vibration signal acquisition of rolling element bearings

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Fig. 10

Bearing vibration signals with different health statuses: (a) waveform and (b) spectrum of healthy signal, (c) waveform and (d) spectrum of outer race defect signal, (e) waveform and (f) spectrum of inner race defect signal, (g) waveform and (h) spectrum of rolling element defect signal

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Fig. 17

Demonstration of multiscale signature of IMFs of bearing signals for the combined defect (outer-race plus inner-race defect)

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Fig. 16

Effect of defect severity on the multiscale slope feature for rolling element defect

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Fig. 15

Effect of defect severity on the multiscale slope feature for inner race defect

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Fig. 14

Effect of defect severity on the multiscale slope feature for outer race defect

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Fig. 13

Bearing vibration signals with different defect severity conditions in the outer race defect: (a) waveform and (b) spectrum for healthy signal, (c) waveform and (d) spectrum for 0.1778 mm defect, (e) waveform and (f) spectrum for 0.3556 mm defect, (g) waveform and (h) spectrum for 0.5334 mm defect

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Fig. 12

Multiscale slope feature values for four different health statuses of the rolling bearing

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Fig. 11

Multiscale signature of IMFs of bearing signals with different health statuses



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