Wind turbine gearbox failure monitoring based on SCADA data analysis

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

8 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationIEEE Power and Energy Society General Meeting
PublisherInstitute of Electrical and Electronics Engineers, Inc.
Volume2016-November
ISBN (print)9781509041688
Publication statusPublished - 10 Nov 2016

Publication series

Name
Volume2016-November
ISSN (Print)1944-9925
ISSN (electronic)1944-9933

Conference

Title2016 IEEE Power and Energy Society General Meeting, PESGM 2016
PlaceUnited States
CityBoston
Period17 - 21 July 2016

Link(s)

DOI

DOI
Check@CityULib
Link to Scopus https://www.scopus.com/record/display.uri?eid=2-s2.0-85001790756&origin=recordpage
Permanent Link https://scholars.cityu.edu.hk/en/publications/publication(30b07528-6e13-40ae-9e9e-98e4ea846bca).html

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

Fingerprint

Publication fingerprints text

100
Compressed Air Moto... Engineering
100
Supervisory Control Engineering
100
Gearbox Engineering
100
Process Control Material Science
28
Deep Neural Network Engineering
14
Control Charts Engineering
14
Statistical Process... Engineering
14
Network Model Engineering
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Citation Format(s)

[ RIS ] [ BibTeX ]
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 Institute of Electrical and Electronics Engineers, Inc., 2016. 7741571.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review