Wind turbine gearbox fault prediction algorithm based on feature subspace

Yubin Pan, Rongjing Hong, Jie Chen, Hua Wang

科研成果: 会议稿件论文同行评审

摘要

As to wind turbine gearbox, vibration signals are usually non-stationary and non-Gaussion feature, which makes it very difficult to extract useful information. In this paper, we propose a wind turbine gearbox fault prediction algorithms using the feature subspace based kernel principal component analysis (FS-KPCA) and exponentially weighted moving average (EWMA). After that, the vibration signal in normal operation is usedto build the feature subspace where the current monitoring data are mapped to. Then,the detection is achieved viacomparing the hyper-plane defined by the orthogonal basis between the reference and a current state of the system through the concept of subspace angle.Subspace angle is used as an indicator to detect the fault and its threshold is defined by SPC(Statistical Process Control) principle. EWMA is employedto improve the sensitivity of the detection method and reduce false alarms. Finally, the proposed method is applied to the actual wind turbine, which further demonstrate the effectiveness.

源语言英语
出版状态已出版 - 2017
活动1st World Congress on Condition Monitoring 2017, WCCM 2017 - London, 英国
期限: 13 6月 201716 6月 2017

会议

会议1st World Congress on Condition Monitoring 2017, WCCM 2017
国家/地区英国
London
时期13/06/1716/06/17

指纹

探究 'Wind turbine gearbox fault prediction algorithm based on feature subspace' 的科研主题。它们共同构成独一无二的指纹。

引用此