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Research Papers

Effect of the Denoising on Acoustic Emission Signals

[+] Author and Article Information
X. Chiementin1

GRESPI, Groupe de Recherche En Sciences Pour l'Ingénieur, Université de Reims Champagne-Ardenne, Moulin de la Housse, 51687 Reims Cedex 2, Francexavier.chiementin@univ-reims.fr

D. Mba, B. Charnley

Turbomachinery Group, School of Engineering, Cranfield University, Cranfield, Bedfordshire MK43 0AL, UK

S. Lignon, J. P. Dron

GRESPI, Groupe de Recherche En Sciences Pour l'Ingénieur, Université de Reims Champagne-Ardenne, Moulin de la Housse, 51687 Reims Cedex 2, France

1

Corresponding author.

J. Vib. Acoust 132(3), 031009 (Apr 28, 2010) (9 pages) doi:10.1115/1.4000789 History: Received May 20, 2009; Revised October 08, 2009; Published April 28, 2010; Online April 28, 2010

The acoustic emission (AE) technology is growing in its applicability to bearing defect diagnosis. Several publications have shown its effectiveness for earlier detection of bearing defects than vibration analysis. In the latter instance, detection and monitoring of defects can be achieved through temporal statistical indicators, which can further be improved by application of denoising techniques. This paper investigates the application of temporal statistical indicators for AE detection of bearing defects on a purposely built test-rig and assesses the effectiveness of various denoising techniques in improving sensitivity to early defect detection. It is concluded that the denoising methods offer significant improvements in identifying defects with AE, especially the self-adaptive noise cancellation method.

Copyright © 2010 by American Society of Mechanical Engineers
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References

Figures

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Figure 12

Influence of the noise model (reference is kurtosis: 16.78, RMS: 0.0082, crest value: 0.11, crest factor: 14.45, and factor K: 0.00087)

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Figure 13

Influence of the thresholding (reference is kurtosis: 47.47, RMS: 0.0078, crest value: 0.11, crest factor: 22.41, factor K: 0.00074) and of rescaling thresholding (reference is kurtosis: 47.47, RMS: 0.0078, crest value: 0.11, crest factor: 22.41, factor K: 0.00074)

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Figure 14

Signal OD6 and denoised signal by wavelet (sym3, lvl5, donoho sqt2log, one, and soft)

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Figure 15

Evolution of the temporal indicators on denoised signals by SANC method

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Figure 16

Evolution of the temporal indicators on denoised signals by spectral subtraction method

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Figure 17

Evolution of the temporal indicators on denoised signals by wavelet method

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Figure 11

Influence of the mother wavelet (reference is kurtosis: 37.23, RMS: 0.0052, crest value: 0.11, crest factor: 21.69, and factor K: 0.00055) and of the decomposition level (reference is kurtosis: 51.66, RMS: 0.0082, crest value: 0.11, crest factor: 21.69, and factor K: 0.00087)

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Figure 1

Self-adaptive noise cancellation

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Figure 2

Filter bank, decomposition DWT (cA0=y(n))

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Figure 3

Experimental test-rig

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Figure 4

Normalized indicators for varying defect size and outer race (reference is kurtosis: 34.27, RMS: 0.0083, crest value, 0.095, crest factor: 19.05, and factor K: 0.00080)

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Figure 5

Normalized indicators for varying defect size and inner race. (reference is kurtosis: 34.27, RMS: 0.0083, crest value: 0.095, crest factor: 19.05, and factor K: 0.00080)

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Figure 6

Schematic view, showing entry and exit surface condition on seeded defect

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Figure 7

Influence of delay (order=10), (reference is kurtosis: 43.07, RMS: 0.0076, crest value: 0.16, crest factor: 24.41, and factor K: 0.0012) and influence of the order (delay=10) (reference is kurtosis: 102.59, RMS: 0.037, crest value: 0.63, crest factor: 24.45, and factor K: 0.023)

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Figure 8

Signal OD6 and denoised signal by SANC (order=110,  delay=50)

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Figure 9

Influence of number of frames (reference is kurtosis: 30.97, RMS: 0.0054, crest value: 0.082, crest factor: 15.46, factor K: 0.00044) and of forgetting factor (reference is kurtosis: 28.99, RMS: 0.0075, crest value: 0.088, crest factor: 15.46, and factor K: 0.00067)

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Figure 10

Signal OD6 and denoised signal by spectral subtraction (M=2048,  l=0.99).

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