Wind turbine gearbox failure monitoring based on SCADA data analysis

Long Wang; Huan Long; Zijun Zhang; Jia Xu; Ruihua Liu

doi:10.1109/PESGM.2016.7741571

Wind turbine gearbox failure monitoring based on SCADA data analysis

Long Wang, Huan Long, Zijun Zhang^*, Jia Xu, Ruihua Liu

^*Corresponding author for this work

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

8 Citations (Scopus)

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.

Original language	English
Title of host publication	IEEE Power and Energy Society General Meeting
Publisher	IEEE
Volume	2016-November
ISBN (Print)	9781509041688
DOIs	https://doi.org/10.1109/PESGM.2016.7741571
Publication status	Published - 10 Nov 2016
Event	2016 IEEE Power and Energy Society General Meeting, PESGM 2016 - Boston, United States Duration: 17 Jul 2016 → 21 Jul 2016

Publication series

Name
Volume	2016-November
ISSN (Print)	1944-9925
ISSN (Electronic)	1944-9933

Conference

Conference	2016 IEEE Power and Energy Society General Meeting, PESGM 2016
Country/Territory	United States
City	Boston
Period	17/07/16 → 21/07/16

Research Keywords

Data mining
Deep neural network
Gearbox monitoring
SCADA data
Statistical process control

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access Output

10.1109/PESGM.2016.7741571

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

@inproceedings{30b075286e1340ae9e9e98e4ea846bca,

title = "Wind turbine gearbox failure monitoring based on SCADA data analysis",

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.",

keywords = "Data mining, Deep neural network, Gearbox monitoring, SCADA data, Statistical process control",

author = "Long Wang and Huan Long and Zijun Zhang and Jia Xu and Ruihua Liu",

year = "2016",

month = nov,

day = "10",

doi = "10.1109/PESGM.2016.7741571",

language = "English",

isbn = "9781509041688",

volume = "2016-November",

publisher = "IEEE",

booktitle = "IEEE Power and Energy Society General Meeting",

address = "United States",

note = "2016 IEEE Power and Energy Society General Meeting, PESGM 2016 ; Conference date: 17-07-2016 Through 21-07-2016",

}

TY - GEN

T1 - Wind turbine gearbox failure monitoring based on SCADA data analysis

AU - Wang, Long

AU - Long, Huan

AU - Zhang, Zijun

AU - Xu, Jia

AU - Liu, Ruihua

PY - 2016/11/10

Y1 - 2016/11/10

N2 - 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.

AB - 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.

KW - Data mining

KW - Deep neural network

KW - Gearbox monitoring

KW - SCADA data

KW - Statistical process control

UR - http://www.scopus.com/inward/record.url?scp=85001790756&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85001790756&origin=recordpage

U2 - 10.1109/PESGM.2016.7741571

DO - 10.1109/PESGM.2016.7741571

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781509041688

VL - 2016-November

BT - IEEE Power and Energy Society General Meeting

PB - IEEE

T2 - 2016 IEEE Power and Energy Society General Meeting, PESGM 2016

Y2 - 17 July 2016 through 21 July 2016

ER -

Wind turbine gearbox failure monitoring based on SCADA data analysis

Abstract

Publication series

Conference

Research Keywords

UN SDGs

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Cite this