Time-series deep learning fault detection with the application of wind turbine benchmark

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

In this paper, a deep learning fault detection approach is proposed based on the convolutional neural network in order to cope with one class of faults in wind turbine systems. Fault detection is very vital in nowadays industries due to the fact that instantly detection can prevent waste of cost and time. Deep learning as one of the powerful approaches in machine learning is a promising method to identify and classify the intrigued problems, which are hard to solve by classical methods. In this case, less than 5% performance reduction in generator torque along with sensor noise, which is challenging to identify by an operator or classical diagnosis methods is studied. The proposed algorithm, which is evolved from convolutional neural network idea, is evaluated in simulation based on a 4.8 MW wind turbine benchmark and the accuracy of the results confirms the persuasive performance of the suggested approach.

Original languageEnglish
Title of host publication2019 IEEE 17th International Conference on Industrial Informatics (INDIN)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages1337-1342
Number of pages6
ISBN (Electronic)9781728129273
DOIs
Publication statusPublished - Jul 2019
Event17th IEEE International Conference on Industrial Informatics, INDIN 2019 - Helsinki-Espoo, Finland
Duration: 22 Jul 201925 Jul 2019

Publication series

NameIEEE International Conference on Industrial Informatics (INDIN)
Volume2019-July
ISSN (Print)1935-4576

Conference

Conference17th IEEE International Conference on Industrial Informatics, INDIN 2019
CountryFinland
CityHelsinki-Espoo
Period22/07/1925/07/19

Fingerprint Dive into the research topics of 'Time-series deep learning fault detection with the application of wind turbine benchmark'. Together they form a unique fingerprint.

Cite this