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Labelling Drifts in a Fault Detection System for Wind Turbine Maintenance

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Intelligent Distributed Computing XII (IDC 2018)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 798))

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Abstract

A failure detection system is the first step towards predictive maintenance strategies. A popular data-driven method to detect incipient failures and anomalies is the training of normal behaviour models by applying a machine learning technique like feed-forward neural networks (FFNN) or extreme learning machines (ELM). However, the performance of any of these modelling techniques can be deteriorated by the unexpected rise of non-stationarities in the dynamic environment in which industrial assets operate. This unpredictable statistical change in the measured variable is known as concept drift. In this article a wind turbine maintenance case is presented, where non-stationarities of various kinds can happen unexpectedly. Such concept drift events are desired to be detected by means of statistical detectors and window-based approaches. However, in real complex systems, concept drifts are not as clear and evident as in artificially generated datasets. In order to evaluate the effectiveness of current drift detectors and also to design an appropriate novel technique for this specific industrial application, it is essential to dispose beforehand of a characterization of the existent drifts. Under the lack of information in this regard, a methodology for labelling concept drift events in the lifetime of wind turbines is proposed. This methodology will facilitate the creation of a drift database that will serve both as a training ground for concept drift detectors and as a valuable information to enhance the knowledge about maintenance of complex systems.

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Acknowledgements

This research has been supported by NEM Solutions, a technology-based company focused that provides intelligent maintenance of complex systems to O&M businesses.

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Authors and Affiliations

  1. NEM Solutions, 20009, San Sebastian, Spain

    Iñigo Martinez & Iñaki Cabrejas

  2. University of Navarra - Tecnun, 20018, San Sebastian, Spain

    Elisabeth Viles

Authors
  1. Iñigo Martinez
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  2. Elisabeth Viles
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  3. Iñaki Cabrejas
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Corresponding author

Correspondence to Iñigo Martinez .

Editor information

Editors and Affiliations

  1. Basque Center for Applied Mathematics (BCAM), TECNALIA, University of the Basque Country (UPV/EHU), Derio, Bizkaia, Spain

    Javier Del Ser

  2. TECNALIA, Derio, Bizkaia, Spain

    Eneko Osaba

  3. University of the Basque Country (UPV/EHU), Bilbao, Bizkaia, Spain

    Miren Nekane Bilbao

  4. Computer Science Department, University of Las Palmas de Gran Canaria, Las Palmas, Spain

    Javier J. Sanchez-Medina

  5. OpenIoT Reesarch Unit, FBK CREATE-NET, Trento, Italy

    Massimo Vecchio

  6. Department of Design Engineering and Mathematics, Middlesex University, London, UK

    Xin-She Yang

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Martinez, I., Viles, E., Cabrejas, I. (2018). Labelling Drifts in a Fault Detection System for Wind Turbine Maintenance. In: Del Ser, J., Osaba, E., Bilbao, M., Sanchez-Medina, J., Vecchio, M., Yang, XS. (eds) Intelligent Distributed Computing XII. IDC 2018. Studies in Computational Intelligence, vol 798. Springer, Cham. https://doi.org/10.1007/978-3-319-99626-4_13

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  • DOI: https://doi.org/10.1007/978-3-319-99626-4_13

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