Adaptive training of a vibration-based anomaly detector for wind turbine condition monitoring system (CMS) is carried out to achieve high-performance detection from the early stages of monitoring. Machine learning-based wind turbine CMSs are required to collect large-scale data to yield reliable predictions. Existing studies in this area have postulated that both data for training a monitoring system and those during the operation of the system are obtained from identical devices. In addition, constant monitoring of data is desir-able, but in practice, the data can be observed periodically (e.g., several tens of seconds of data are observed every two hours). In this case, collecting sufficient data is time con-suming, making it difficult to conduct accurate predictions at the early stage of the CMS operation. To address this prob-lem, a small amount of vibration data observed at a target wind turbine is utilized to adapt the anomaly detector that is trained on relatively large-scale vibration signals obtained from other wind turbines. In the present study, maximum a posteriori (MAP) adaptation is applied to a Gaussian mixture model (GMM)-based anomaly detector. Experimental comparisons using vibration data from the gearbox in the experimental environment and those used in the wind turbine demonstrated that MAP-based GMM adaptation yielded an improvement in anomaly detection accuracy even when only a small amount of data is observed at the target gearbox.
|ジャーナル||International Journal of Prognostics and Health Management|
|出版ステータス||Published - 2017|
ASJC Scopus subject areas
- コンピュータ サイエンス（その他）