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Rolling Element Bearing Fault Diagnosis Using Laplace-Wavelet Envelope Power Spectrum

EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 073629 (2007) Cite this article

Abstract

The bearing characteristic frequencies (BCF) contain very little energy, and are usually overwhelmed by noise and higher levels of macro-structural vibrations. They are difficult to find in their frequency spectra when using the common technique of fast fourier transforms (FFT). Therefore, Envelope Detection (ED) has always been used with FFT to identify faults occurring at the BCF. However, the computation of the ED is suffering to strictly define the resonance frequency band. In this paper, an alternative approach based on the Laplace-wavelet enveloped power spectrum is proposed. The Laplace-Wavelet shape parameters are optimized based on Kurtosis maximization criteria. The results for simulated as well as real bearing vibration signal show the effectiveness of the proposed method to extract the bearing fault characteristic frequencies from the resonant frequency band.

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Authors and Affiliations

  1. Department of Mechanical and Industrial Engineering, Caledonian College of Engineering, P.O. Box 2322, CPO Seeb, PC, 111, Oman

    Khalid F. Al-Raheem & K. P. Ramachandran

  2. School of Engineering Science and Design, Glasgow Caledonian University, Glasgow, G40BA, UK

    Asok Roy, D. K. Harrison & Steven Grainger

Authors
  1. Khalid F. Al-Raheem
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  2. Asok Roy
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  3. K. P. Ramachandran
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  4. D. K. Harrison
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  5. Steven Grainger
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Correspondence to Khalid F. Al-Raheem.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Al-Raheem, K.F., Roy, A., Ramachandran, K.P. et al. Rolling Element Bearing Fault Diagnosis Using Laplace-Wavelet Envelope Power Spectrum. EURASIP J. Adv. Signal Process. 2007, 073629 (2007). https://doi.org/10.1155/2007/73629

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  • DOI: https://doi.org/10.1155/2007/73629

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