Fault Diagnosis of Wind Turbine Gearbox Based on KELM and Multi-sensor Information Fusion

被引：0

作者：

Long X. ^{[1
]}

Yang P. ^{[2
]}

Guo H. ^{[1
]}

Zhao Z. ^{[3
]}

机构：

[1] School of Electric Power, South China University of Technology, Guangzhou

[2] Guangdong Key Laboratory of Clean Energy Technology, South China University of Technology, Guangzhou

[3] School of Automation, Guangdong University of Technology, Guangzhou

[4] State Grid Xinjiang Electric Power Maintenance Company, Urumqi

来源：

Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2019年 / 43卷 / 17期

关键词：

Condition monitoring; Grey wolf optimization-based kernel extreme learning machine (GWO-KELM); Multi-sensor information fusion; Wind turbine gearbox;

D O I：

10.7500/AEPS20181126005

中图分类号：

学科分类号：

摘要：

To improve the operation efficiency of wind turbine gearbox (WTB) and reduce the operation and maintenance costs of wind farm, a novel condition monitoring method of grey wolf optimization-based kernel extreme learning machine (GWO-KELM) is proposed, which combines time-domain statistical feature analysis and multi-sensor information fusion technology. Firstly, different time-domain indicator feature parameters of the original vibration signal are calculated, and a fusion data set from the feature level and the data level can be obtained by means of parallel superposition. Secondly, a WTB fault classification recognition model based on GWO-KELM is established using the fusion data set. Finally, combining with the measured gearbox data of QPZZ-Ⅱ rotating mechanical vibration test bench, the proposed method is adopted to realize the gearbox fault monitoring. The example results show the effectiveness and feasibility of the proposed method, and it has the best classification performance compared with other similar methods. © 2019 Automation of Electric Power Systems Press.

引用

页码：132 / 139

页数：7

共 34 条

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