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Failure Diagnosis of Wind Turbine Bearing Using Feature Extraction and a Neuro-Fuzzy Inference System (ANFIS)

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Advances in Computational Intelligence (IWANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11506))

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Abstract

Bearing failures are the most common type of malfunction in wind turbines. As such, isolating these defects enables maintenance scheduling in advance; hence, preventing further damage to turbines. This paper introduces a new fault detection and diagnosis (FDD) method to isolate two types of bearing failures in Wind turbines (WTs). The proposed FDD method consists of a feature extraction/feature selection and an adaptive neuro-fuzzy inference system (ANFIS) method. The feature extraction and selection phase identifies proper features to capture the nonlinear dynamics of the failure. Then, the ANFIS classifier diagnoses the failure type using the extracted features. Several experimental test studies with the historical data of wind farms in South-western Ontario are performed to evaluate the performance of the FDD system. Test results indicate that the proposed monitoring system is accurate and effective.

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Acknowledgment

The authors would like to acknowledge that this work is part of the YR21 Investment Decision Support Program supported by progressive industrial partners, Natural Sciences and Engineering Research Council (NSERC) of Canada, and the Ontario Centres of Excellence (OCE).

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Authors and Affiliations

  1. Electrical Department, University of Windsor, Windsor, ON, Canada

    Mojtaba Kordestani & Mehrdad Saif

  2. Turbulence and Energy Laboratory, University of Windsor, Windsor, ON, Canada

    Milad Rezamand, Rupp Carriveau & David S. K. Ting

Authors
  1. Mojtaba Kordestani
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  2. Milad Rezamand
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  3. Rupp Carriveau
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  4. David S. K. Ting
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  5. Mehrdad Saif
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Corresponding author

Correspondence to Mehrdad Saif .

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Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Malaga, Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Barcelona, Spain

    Andreu Catala

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Kordestani, M., Rezamand, M., Carriveau, R., Ting, D.S.K., Saif, M. (2019). Failure Diagnosis of Wind Turbine Bearing Using Feature Extraction and a Neuro-Fuzzy Inference System (ANFIS). In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11506. Springer, Cham. https://doi.org/10.1007/978-3-030-20521-8_45

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  • DOI: https://doi.org/10.1007/978-3-030-20521-8_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20520-1

  • Online ISBN: 978-3-030-20521-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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