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Comparison between sound perception and self-organizing maps in the monitoring of the bearing degradation

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Abstract

This study aims to monitor and detect bearing defects from measured signals on a wind turbine during 50 operating days using two methods. The first method involves a perceptual approach to classify the selected signals based on 50 measurements. The second method used is an unsupervised classification method called the Self-Organizing Map (SOM). Overall, the perceptive approach proved to be simple and effective compared to conventional methods of treatment and diagnosis of defects, as listeners were able to classify the selected sounds in the order of bearing degradation, allowing the severity of the bearing defect to be tracked. Furthermore, the neural classifier provided relevant information on the evolution of bearing degradation, as it could automatically cluster the vibration signal into four groups corresponding to the bearing life stages. Thus, these results can effectively contribute to well-timed maintenance decisions. In addition, the advantages and deficiencies of one method over the other are briefly discussed in this paper.

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Acknowledgements

Acknowledgements are made for the measurements used in this work provided through http://data-acoustics.com/Database.

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Authors and Affiliations

  1. Laboratoire Mécanique Appliquée et Ingénierie MAI, Ecole Nationale d’Ingénieurs de Tunis- ENIT, BP 37, Le Belvedere, 1002, Tunis, Tunisia

    Saiefeddine Alia & Rachid Nasri

  2. Ecole Nationale Supérieure de Technologie, Dergana Bordj El Kiffan, Algiers, Algeria

    Ikhlas Meddour

  3. Mechanics and Structures Laboratory, Université8 Mai 1945 Guelma, BP 401, 24000, Guelma, Algeria

    Ikhlas Meddour & Ramdane Younes

  4. Mechanical Engineering Department, Badji Mokhtar University, P.O. Box 12, 23000, Annaba, Algeria

    Ramdane Younes

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  1. Saiefeddine Alia
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  2. Rachid Nasri
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  3. Ikhlas Meddour
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  4. Ramdane Younes
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Correspondence to Ramdane Younes.

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Alia, S., Nasri, R., Meddour, I. et al. Comparison between sound perception and self-organizing maps in the monitoring of the bearing degradation. Int J Adv Manuf Technol 110, 2003–2013 (2020). https://doi.org/10.1007/s00170-020-06009-y

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  • DOI: https://doi.org/10.1007/s00170-020-06009-y

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