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Motif Analysis in Internet of the Things Platform for Wind Turbine Maintenance Management

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Proceedings of the Fifteenth International Conference on Management Science and Engineering Management (ICMSEM 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 78))

Abstract

Wind energy is one of the most competitive renewable energy sources. Supervisory control and data acquisition system provides alarm activations in case of failure, and also signals of the system. Due to the volume and different type of data, these systems require advanced analytics to ensure a suitable maintenance management. Several methods are employed, mainly based in artificial intelligence that involve advanced trainings and elevated computational costs with high possibilities to detect false positives. The novelty proposed in this work is based on motif analysis using an Internet of the Things platform to analyze large time series data for wind turbine monitoring. It is presented an approach considering personalized motifs in specific periods of the signal dataset with more influence in the alarm activation. A real case study is presented analyzing periods before historical alarm activation to forecast relevant trends in time series data. The results obtained with the proposed method provide high accuracy, where this information can be implanted in the maintenance management plan.

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Acknowledgements

The work reported herewith has been financially by the Dirección General de Universidades, Investigaciónn e Innovaciónn of Castilla-La Mancha, under Research Grant ProSeaWind project (Ref.: SBPLY/19/180501/000102).

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Authors and Affiliations

  1. Ingenium Research Group, Universidad Castilla-La Mancha, 13071, Ciudad Real, Spain

    Isaac Segovia Ramirez, Eduardo Cruz Urioso, Ana María Peco & Fausto Pedro García Márquez

  2. Trendalyze Inc., Newark, NJ, 07102, USA

    Rado Kotorov, Raunaq G. Padhye & Amanjeet S. Bhatia

  3. Department of Computer Science and IT, La Trobe University, Melbourne, Australia

    Lianhua Chi

  4. Universidad Europea de Madrid, Madrid, Spain

    Carlos Quiterio Gomez Muñoz

Authors
  1. Isaac Segovia Ramirez
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  2. Eduardo Cruz Urioso
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  3. Ana María Peco
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  4. Rado Kotorov
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  5. Lianhua Chi
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  6. Raunaq G. Padhye
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  7. Amanjeet S. Bhatia
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  8. Carlos Quiterio Gomez Muñoz
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  9. Fausto Pedro García Márquez
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Corresponding author

Correspondence to Isaac Segovia Ramirez .

Editor information

Editors and Affiliations

  1. Business School, Sichuan University, Chengdu, China

    Jiuping Xu

  2. University of Castilla-La Mancha (UCLM), Ciudad Real, Ciudad Real, Spain

    Fausto Pedro García Márquez

  3. World Academy of Sciences, Trieste, Italy

    Mohamed Hag Ali Hassan

  4. Academy of Sciences of Moldova, Chisinau, Moldova

    Gheorghe Duca

  5. Institute of Systems Control, Azerbaijan National Academy of Sciences, Baku, Azerbaijan

    Asaf Hajiyev

  6. Department of Industrial Engineering, Gazi University, Ankara, Turkey

    Fulya Altiparmak

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Ramirez, I.S. et al. (2021). Motif Analysis in Internet of the Things Platform for Wind Turbine Maintenance Management. In: Xu, J., García Márquez, F.P., Ali Hassan, M.H., Duca, G., Hajiyev, A., Altiparmak, F. (eds) Proceedings of the Fifteenth International Conference on Management Science and Engineering Management. ICMSEM 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 78. Springer, Cham. https://doi.org/10.1007/978-3-030-79203-9_7

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