Adaptive training of vibration-based anomaly detector for wind turbine condition monitoring

Takanori Hasegawa, Jun Ogata, Masahiro Murakawa, Tetsunori Kobayashi, Tetsuji Ogawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Adaptive training of a vibration-based anomaly detector forwind turbine condition monitoring system (CMS) is carriedout to achieve high-performance detection from the earlystages of monitoring. Machine learning-based wind turbineCMSs are required to collect large-scale data to yield reliablepredictions. Existing studies in this area have postulatedthat both data for training a monitoring system and those duringthe operation of the system are obtained from identicaldevices. In addition, constant monitoring of data is desirable,but in practice, the data can be observed periodically(e.g., several tens of seconds of data are observed every twohours). In this case, collecting sufficient data is time consuming,making it difficult to conduct accurate predictions atthe early stage of the CMS operation. To address this problem,a small amount of vibration data observed at a targetwind turbine is utilized to adapt the anomaly detector thatis trained on relatively large-scale vibration signals obtainedfrom other wind turbines. In the present study, maximum aposteriori (MAP) adaptation is applied to a Gaussian mixturemodel (GMM)-based anomaly detector. Experimentalcomparisons using vibration data from the gearbox in the experimentalenvironment and those used in the wind turbinedemonstrated that MAP-based GMM adaptation yielded animprovement in anomaly detection accuracy even when onlya small amount of data is observed at the target gearbox.

Original languageEnglish
Title of host publicationPHM 2017 - Proceedings of the Annual Conference of the Prognostics and Health Management Society 2017
EditorsMatthew J. Daigle, Anibal Bregon
PublisherPrognostics and Health Management Society
Pages177-184
Number of pages8
ISBN (Electronic)9781936263059
Publication statusPublished - 2017 Jan 1
Event9th Annual Conference of the Prognostics and Health Management Society, PHM 2017 - St. Petersburg, United States
Duration: 2017 Oct 22017 Oct 5

Publication series

NameProceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
ISSN (Print)2325-0178

Conference

Conference9th Annual Conference of the Prognostics and Health Management Society, PHM 2017
CountryUnited States
CitySt. Petersburg
Period17/10/217/10/5

ASJC Scopus subject areas

  • Information Systems
  • Electrical and Electronic Engineering
  • Health Information Management
  • Computer Science Applications

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  • Cite this

    Hasegawa, T., Ogata, J., Murakawa, M., Kobayashi, T., & Ogawa, T. (2017). Adaptive training of vibration-based anomaly detector for wind turbine condition monitoring. In M. J. Daigle, & A. Bregon (Eds.), PHM 2017 - Proceedings of the Annual Conference of the Prognostics and Health Management Society 2017 (pp. 177-184). (Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM). Prognostics and Health Management Society.