KLI

Machine learning-based scheme for multi-class fault detection in turbine engine disks

Metadata Downloads
Alternative Title
Machine learning-based scheme for multi-class fault detection in turbine engine disks
Abstract
Fault detection of rotating engine components in the aircraft engine is a challenging task that must constantly be monitored to provide aviation safety. In this paper, we propose a novel approach based on multi-layer perceptron (MLP) to detect in real time the degree of faults in a turbine engine disk due to a crack. To further improve detection accuracy while reducing computational complexity, the recursive feature elimination (RFE) is applied as a potent feature selection method. Satisfactorily, simulation results show that the proposed framework is robust to changes in operating conditions and outperforms comparative approaches.
Author(s)
가르시아 모레타 카를라 에스테구인수카마나 아코스타 마리오 로드리
Issued Date
2021
Type
Article
Keyword
Fault detectionMulti-layer perceptron (MLP)Recursive feature elimination (RFE)Turbine engine disk
DOI
10.1016/j.icte.2021.01.009
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9049
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_798d9938a6634383a8e8a55459d86e7a&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Machine%20learning-based%20scheme%20for%20multi-class%20fault%20detection%20in%20turbine%20engine%20disks&offset=0&pcAvailability=true
Publisher
ICT EXPRESS
Location
네덜란드
Language
영어
ISSN
2405-9595
Citation Volume
7
Citation Number
1
Citation Start Page
15
Citation End Page
22
Appears in Collections:
Engineering > IT Convergence
Authorize & License
  • Authorize공개
Files in This Item:
  • There are no files associated with this item.

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.