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Automatic condition monitoring system for crack detection in rotating machinery

Author

Listed:
  • Gómez, M.J.
  • Castejón, C.
  • García-Prada, J.C.

Abstract

Maintenance is essential to prevent catastrophic failures in rotating machinery. A crack can cause a failure with costly processes of reparation, especially in a rotating shaft.

Suggested Citation

  • Gómez, M.J. & Castejón, C. & García-Prada, J.C., 2016. "Automatic condition monitoring system for crack detection in rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 239-247.
    • Handle: RePEc:eee:reensy:v:152:y:2016:i:c:p:239-247
    DOI: 10.1016/j.ress.2016.03.013
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    References listed on IDEAS

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    1. Herzog, M.A. & Marwala, T. & Heyns, P.S., 2009. "Machine and component residual life estimation through the application of neural networks," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 479-489.
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    3. Fink, Olga & Zio, Enrico & Weidmann, Ulrich, 2014. "Predicting component reliability and level of degradation with complex-valued neural networks," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 198-206.
    4. Santosh, T.V. & Srivastava, A. & Sanyasi Rao, V.V.S. & Ghosh, A.K. & Kushwaha, H.S., 2009. "Diagnostic system for identification of accident scenarios in nuclear power plants using artificial neural networks," Reliability Engineering and System Safety, Elsevier, vol. 94(3), pages 759-762.
    5. Kim, Kyungmee O. & Zuo, Ming J., 2007. "Two fault classification methods for large systems when available data are limited," Reliability Engineering and System Safety, Elsevier, vol. 92(5), pages 585-592.
    6. Martinez-Martinez, Sinuhe & Messai, Nadhir & Jeannot, Jean-Philippe & Nuzillard, Danielle, 2015. "Two neural network based strategies for the detection of a total instantaneous blockage of a sodium-cooled fast reactor," Reliability Engineering and System Safety, Elsevier, vol. 137(C), pages 50-57.
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