Wind turbine gearbox failure monitoring based on SCADA data analysis
Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with ISBN/ISSN) › peer-review
Author(s)
Detail(s)
| Original language | English |
|---|---|
| Title of host publication | IEEE Power and Energy Society General Meeting |
| Publisher | IEEE |
| Volume | 2016-November |
| ISBN (Print) | 9781509041688 |
| Publication status | Published - 10 Nov 2016 |
Publication series
| Name | |
|---|---|
| Volume | 2016-November |
| ISSN (Print) | 1944-9925 |
| ISSN (Electronic) | 1944-9933 |
Conference
| Title | 2016 IEEE Power and Energy Society General Meeting, PESGM 2016 |
|---|---|
| Place | United States |
| City | Boston |
| Period | 17 - 21 July 2016 |
Link(s)
Abstract
A model for monitoring the wind turbine gearbox based on Supervisory Control and Data Acquisition (SCADA) data is developed. A deep neural network (DNN) is trained with the data of normal gearboxes to predict its performance. The developed DNN model is next tested with data of the normal and abnormal gearboxes. The abnormal behavior of the gearbox can be detected by the statistical process control charts via the fitting error. The capacity of the monitoring model for detecting the abnormal behavior of gearbox is validated by two gearbox failure cases.
Research Area(s)
- Data mining, Deep neural network, Gearbox monitoring, SCADA data, Statistical process control
Citation Format(s)
Wind turbine gearbox failure monitoring based on SCADA data analysis. / Wang, Long; Long, Huan; Zhang, Zijun et al.
IEEE Power and Energy Society General Meeting. Vol. 2016-November IEEE, 2016. 7741571.Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with ISBN/ISSN) › peer-review
