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Research Papers

Experimental Evaluation of a Unified Time-Scale-Frequency Technique for Bearing Defect Feature Extraction

[+] Author and Article Information
Ruqiang Yan

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

Robert X. Gao1

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

Changting Wang

Global Research Center, General Electric Corporation, Niskayuna, NY 12309

1

Corresponding author.

J. Vib. Acoust 131(4), 041012 (Jun 18, 2009) (10 pages) doi:10.1115/1.3147125 History: Received May 05, 2008; Revised April 07, 2009; Published June 18, 2009

A systematic experimental study is presented in this paper on evaluating the effectiveness of a unified, multidomain algorithm for defect feature extraction in bearing condition monitoring and health diagnosis. The algorithm decomposes vibration signals measured on bearings by discrete wavelet transform and subsequently performs the Fourier transform on the wavelet coefficients. The effectiveness of such a unified technique is demonstrated through experimental case studies, which confirmed its advantage over the wavelet or Fourier transform techniques employed alone. Also, the unified technique has shown to be computationally more efficient than the enveloping technique based on continuous wavelet transform, thus providing a good signal processing tool for bearing defect diagnosis.

Copyright © 2009 by American Society of Mechanical Engineers
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Figures

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Figure 2

Bearing test bed with hydraulic load application capability

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Figure 3

Spectral analysis of signals from a healthy and a defective bearing (speed of 600 rpm, axial load of 7038 N, and radial load of 17,468 N)

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Figure 4

Defect signal analysis using a sixth order Butterworth band-pass filter (speed of 600 rpm, axial load of 7038 N, radial load of 17,468 N, and filter band of 45–70 Hz)

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Figure 5

Envelope analysis of the vibration signal from a defective bearing using continuous wavelet transform (speed of 600 rpm, axial load of 7038 N, radial load of 17,468 N)

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Figure 6

Wavelet decomposition of bearing signals using Daubechies 2 wavelet at decomposition level 7 (speed of 600 rpm, axial load of 7038 N, and radial load of 17,468 N)

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Figure 7

Wavelet decomposition of bearing signals using Daubechies 4 wavelet at decomposition level 7 (speed of 600 rpm, axial load of 7038 N, and radial load of 17,468 N,)

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Figure 8

Wavelet decomposition of bearing signals using Coiflet 1 wavelet at decomposition level 7 (speed of 600 rpm, axial load of 7038 N, and radial load of 17,468 N)

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Figure 9

Wavelet decomposition of bearing signals using biorthogonal 2.2 wavelet at decomposition level 7 (speed of 600 rpm, axial load of 7038 N, and radial load of 17,468 N)

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Figure 10

Unified analysis for defect feature extraction using Daubechies 2 wavelet at decomposition level 7 (speed of 600 rpm, axial load of 7038 N, and radial load of 17,468 N)

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Figure 11

Unified analysis for defect feature extraction using Daubechies 4 wavelet at decomposition level 7 (speed of 600 rpm, axial load of 7038 N, and radial load of 17,468 N)

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Figure 12

Unified analysis for defect feature extraction using Coiflet 1 wavelet at decomposition level 7 (speed of 600 rpm, axial load of 7038 N, and radial load of 17,468 N)

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Figure 13

Unified analysis for defect feature extraction using biorthogonal 2.2 wavelet at decomposition level 7 (speed of 600 rpm, axial load of 7038 N, and radial load of 17,468 N)

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Figure 14

Unified analysis using Daubechies 2 wavelet at levels 6 and 8 (speed of 600 rpm, axial load of 7038 N, and radial load of 17,468 N)

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Figure 15

Effect of the radial load on defect feature amplitude (speed of 600 rpm, axial load of 0 N, and Daubechies 2 wavelet at level 7)

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Figure 16

Effect of axial load on defect feature amplitude (speed of 600 rpm, radial load of 32,753 N, and Daubechies 2 wavelet at level 7)

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Figure 17

Effect of bearing speed on defect amplitude (axial load of 12,279 N, radial load of 13,101 N, and Daubechies 2 wavelet)

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Figure 1

Illustration of the generalized signal transformation scheme

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