NDLI: Adaptive fault detection in wind turbine via RF and CUSUM

Content Provider	IET Digital Library
Author	Xu, Qifa Lu, Shixiang Zhai, Zhongping Jiang, Cuixia
Abstract	Rapid developments of wind industry arise the issue of heavy monitoring tasks. The residual monitoring based on normal behaviour modelling is a highly recommended method when fault record information is missing. However, it is difficult to achieve efficient normal behaviour modelling and dynamic residual monitoring simultaneously. To this end, a novel adaptive fault detection scheme, which merges random forest (RF) with adaptive cumulative sum (CUSUM), is proposed. The authors exploit RF to explore the non-linear mechanism between features and the target variable robustly, and obtain the residuals quickly. Then, they design the adaptive CUSUM control chart of time-varying shift to sensitively detect the changes of residuals. For illustration, they apply the proposed scheme to the supervisory control and data acquisition data acquired from a wind farm in China. The empirical results demonstrate that the proposed scheme is superior to several competing methods in capturing faults and reducing false alarms. Meanwhile, the authors find it can detect anomaly quickly, automatically and robustly under different signal-to-noise ratios. These provide operators sufficient time to adopt an effective maintenance strategy.
Starting Page	1789
Ending Page	1796
Page Count	8
ISSN	17521416
Volume Number	14
e-ISSN	17521424
Issue Number	Issue 10, Jul (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rpg/14/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rpg.2019.0913
Journal	IET Renewable Power Generation
Publisher Date	2020-04-21
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Cumulative Sum Adaptive CUSUM Control Chart Control Charts Data Acquisition Data Acquisition Data Dynamic Residual Monitoring Efficient Normal Behaviour Modelling Fault Fault Diagnosis Fault Record Information Heavy Monitoring Task Highly Recommended Method Inspection And Quality Control Maintenance Engineering Nonlinear Mechanism Novel Adaptive Fault Detection Scheme Random Forest Reducing False Alarms RF Statistical Process Control Statistics Supervisory Control Target Variable Time-varying Shift Wind Farm Wind Industry Wind Power Plant Wind Turbine
Content Type	Text
Resource Type	Article
Subject	Renewable Energy, Sustainability and the Environment

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