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Research Papers

A Nonlinear Noise Reduction Approach to Vibration Analysis for Bearing Health Diagnosis

[+] Author and Article Information
Ruqiang Yan1

School of Instrument Science and Engineering,  Southeast University, Nanjing, Jiangsu 210096, P. R. C.ruqiang@seu.edu.edu

Robert X. Gao

Department of Mechanical Engineering,  University of Connecticut, Storrs, CT 06269rgao@engr.uconn.edu

1

Corresponding author.

J. Comput. Nonlinear Dynam 7(2), 021004 (Jan 06, 2012) (7 pages) doi:10.1115/1.4005463 History: Received June 07, 2011; Revised November 17, 2011; Accepted November 18, 2011; Published January 06, 2012; Online January 06, 2012

This paper presents a local geometric projection (LGP)-based noise reduction technique for vibration signal analysis in rolling bearings. LGP is a nonlinear filtering technique that reconstructs one dimensional time series in a high-dimensional phase space using time-delayed coordinates based on the Takens embedding theorem. From the neighborhood of each point in the phase space, where a neighbor is defined as a local subspace of the whole phase space, the best subspace to which the point will be orthogonally projected is identified. Since the signal subspace is formed by the most significant eigen-directions of the neighborhood, while the less significant ones define the noise subspace, the noise can be reduced by converting the points onto the subspace spanned by those significant eigen-directions back to a new, one-dimensional time series. Improvement on signal-to-noise ratio enabled by LGP is first evaluated using a chaotic system and an analytically formulated synthetic signal. Then, analysis of bearing vibration signals is carried out as a case study. The LGP-based technique is shown to be effective in reducing noise and enhancing extraction of weak, defect-related features, as manifested by both the multi-fractal and envelope spectra of the signal.

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Copyright © 2012 by American Society of Mechanical Engineers
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Figures

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Figure 1

Noise-free Lorenz signal for the x direction

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Figure 2

Lorenz signal with additive white noise (SNR = 20 dB)

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Figure 3

Lorenz signal after noise reduction (SNR = 30.3 dB)

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Figure 4

Simulated defect-induced resonant vibrations

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Figure 5

The synthetic signal before noise reduction (S/N ratio = 4 dB)

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Figure 6

The synthetic signal after noise reduction using LGP (S/N ratio = 10 dB)

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Figure 7

Waveform of the synthetic signal denoised by the Mexican hat wavelet (S/N ratio = 8 dB)

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Figure 8

Customized bearing test system setup

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Figure 9

Experimental vibration signals before noise reduction

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Figure 10

Experimental vibration signals after noise reduction

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Figure 11

Multi-fractal spectra of the bearing vibration signals before noise reduction

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Figure 12

Multi-fractal spectra of the bearing vibration signals after noise reduction

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Figure 13

Comparison on envelope spectra of bearing vibration signals

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