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Detection of rolling bearing defects using discrete wavelet analysis

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Abstract

In the detection of bearing faults the so much desired objective remains the extraction of the defect vibratory signature from the measured signal in which immerses the random noise and other components of the machine. In this article a denoising method of the measured signals is presented. Based on the optimization of wavelet multiresolution analysis, it uses the kurtosis as an optimization and evaluation criterion, several parameters were then selected. The experimental results show the validity of this method within the detection of several defects simulated on ball bearings. The various configurations, in which the signals were measured, allow leading to optimum conditions of its application. The application of WMRA on filtered signals allows better results than its application on wide bands signals or a simple band pass filtering.

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Authors and Affiliations

  1. Laboratory of Mechanics and Structures, University of Guelma, P.B. 401, 24000, Guelma, Algeria

    Abderrazek Djebala & Nouredine Ouelaa

  2. Laboratory of Vibration-Acoustics, INSA of Lyon, Bâtiment A. de St. Exupéry, 25 bis Avenue Jean Capelle, 69621, Villeurbanne cedex, France

    Nacer Hamzaoui

Authors
  1. Abderrazek Djebala
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  2. Nouredine Ouelaa
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  3. Nacer Hamzaoui
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Correspondence to Abderrazek Djebala.

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Djebala, A., Ouelaa, N. & Hamzaoui, N. Detection of rolling bearing defects using discrete wavelet analysis. Meccanica 43, 339–348 (2008). https://doi.org/10.1007/s11012-007-9098-y

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  • DOI: https://doi.org/10.1007/s11012-007-9098-y

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