Wind Turbine Health Monitoring: Current and Future Trends with an Active Learning Twist

被引：1

作者：

Dervilis, N. ^{[1
]}

Maguire, A. E. ^{[2
]}

Papatheou, E. ^{[1
]}

Worden, K. ^{[1
]}

机构：

[1] Univ Sheffield, Dynam Res Grp, Dept Mech Engn, Mappin St, Sheffield S1 3JD, S Yorkshire, England

[2] New Renewables, Vattenfall Res & Dev, Tun Bldg,Holyrood Rd, Edinburgh EH8 8AE, Midlothian, Scotland

来源：

ROTATING MACHINERY, HYBRID TEST METHODS, VIBRO-ACOUSTICS & LASER VIBROMETRY, VOL 8 | 2017年

基金：

英国工程与自然科学研究理事会;

关键词：

Environmental and operational variations; Manifold learning; Pattern recognition; Gaussian processes; SUPPORT VECTOR MACHINE;

D O I：

10.1007/978-3-319-54648-3_13

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

The use of offshore wind farms has been geometrically growing in recent years. Offshore power plants move into deeper waters as European water sites offer impressive wind conditions. However, the cost of an offshore wind farm is relatively high, and therefore, their reliability is crucial if they ever need to be fully integrated into the energy arena. This paper presents an investigation of current monitoring trends for wind turbines (WTs) and will try to address the motivation and the effectiveness of Structural Health Monitoring (SHM) machine learning applications for the different components of a WTs, as well as, the novel idea of intelligent WT in terms of data knowledge transfer and learning.

引用

页码：119 / 129

页数：11

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