IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v152y2016icp239-247.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832016300035
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2016.03.013?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    2. Dai, Hongzhe & Zhang, Boyi & Wang, Wei, 2015. "A multiwavelet support vector regression method for efficient reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 132-139.
    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. 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.
    5. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Santhosh, T.V. & Gopika, V. & Ghosh, A.K. & Fernandes, B.G., 2018. "An approach for reliability prediction of instrumentation & control cables by artificial neural networks and Weibull theory for probabilistic safety assessment of NPPs," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 31-44.
    2. Izquierdo, J. & Márquez, A. Crespo & Uribetxebarria, J. & Erguido, A., 2020. "On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects," Renewable Energy, Elsevier, vol. 153(C), pages 1100-1110.
    3. Roy, Atin & Chakraborty, Subrata, 2022. "Reliability analysis of structures by a three-stage sequential sampling based adaptive support vector regression model," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    4. Li, Xiang & Ding, Qian & Sun, Jian-Qiao, 2018. "Remaining useful life estimation in prognostics using deep convolution neural networks," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 1-11.
    5. Tolo, Silvia & Tian, Xiange & Bausch, Nils & Becerra, Victor & Santhosh, T.V. & Vinod, G. & Patelli, Edoardo, 2019. "Robust on-line diagnosis tool for the early accident detection in nuclear power plants," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 110-119.
    6. Ondemir, Onder & Gupta, Surendra M., 2014. "A multi-criteria decision making model for advanced repair-to-order and disassembly-to-order system," European Journal of Operational Research, Elsevier, vol. 233(2), pages 408-419.
    7. Ammar Y. Alqahtani & Surendra M. Gupta, 2017. "One-Dimensional Renewable Warranty Management within Sustainable Supply Chain," Resources, MDPI, vol. 6(2), pages 1-26, April.
    8. Fang, Xiaolei & Zhou, Rensheng & Gebraeel, Nagi, 2015. "An adaptive functional regression-based prognostic model for applications with missing data," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 266-274.
    9. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    10. Li, Luxin & Chen, Guohai & Fang, Mingxuan & Yang, Dixiong, 2021. "Reliability analysis of structures with multimodal distributions based on direct probability integral method," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    11. Dai, Hongzhe & Zhang, Boyi & Wang, Wei, 2015. "A multiwavelet support vector regression method for efficient reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 132-139.
    12. Zhang, Wei & Li, Xiang & Ma, Hui & Luo, Zhong & Li, Xu, 2021. "Transfer learning using deep representation regularization in remaining useful life prediction across operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    13. Li, Zhanhang & Zhou, Jian & Nassif, Hani & Coit, David & Bae, Jinwoo, 2023. "Fusing physics-inferred information from stochastic model with machine learning approaches for degradation prediction," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    14. Keshtegar, Behrooz & Kisi, Ozgur, 2018. "RM5Tree: Radial basis M5 model tree for accurate structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 49-61.
    15. Zio, Enrico & Di Maio, Francesco, 2010. "A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system," Reliability Engineering and System Safety, Elsevier, vol. 95(1), pages 49-57.
    16. Alexandra Akins & Derek Kultgen & Alexander Heifetz, 2023. "Anomaly Detection in Liquid Sodium Cold Trap Operation with Multisensory Data Fusion Using Long Short-Term Memory Autoencoder," Energies, MDPI, vol. 16(13), pages 1-19, June.
    17. Vazquez, Luis & Blanco, Jesús María & Ramis, Rolando & Peña, Francisco & Diaz, David, 2015. "Robust methodology for steady state measurements estimation based framework for a reliable long term thermal power plant operation performance monitoring," Energy, Elsevier, vol. 93(P1), pages 923-944.
    18. Roy, Atin & Chakraborty, Subrata, 2020. "Support vector regression based metamodel by sequential adaptive sampling for reliability analysis of structures," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    19. Jain, Amit Kumar & Lad, Bhupesh Kumar, 2020. "Prognosticating RULs while exploiting the future characteristics of operating profiles," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    20. Roy, Atin & Chakraborty, Subrata, 2023. "Support vector machine in structural reliability analysis: A review," Reliability Engineering and System Safety, Elsevier, vol. 233(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:152:y:2016:i:c:p:239-247. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.