https://www.mdu.se/

mdu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Aircraft engine performance monitoring and diagnostics based on deep convolutional neural networks
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0001-6101-2863
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-8466-356X
2021 (English)In: Machines, E-ISSN 2075-1702, Vol. 9, no 12, article id 337Article in journal (Refereed) Published
Abstract [en]

The rapid advancement of machine-learning techniques has played a significant role in the evolution of engine health management technology. In the last decade, deep-learning methods have received a great deal of attention in many application domains, including object recognition and computer vision. Recently, there has been a rapid rise in the use of convolutional neural networks for rotating machinery diagnostics inspired by their powerful feature learning and classification capability. However, the application in the field of gas turbine diagnostics is still limited. This paper presents a gas turbine fault detection and isolation method using modular convolutional neural networks preceded by a physics-driven performance-trend-monitoring system. The trend-monitoring system was employed to capture performance changes due to degradation, establish a new baseline when it is needed, and generatefault signatures. The fault detection and isolation system was trained to step-by-step detect and classify gas path faults to the component level using fault signatures obtained from the physics part. The performance of the method proposed was evaluated based on different fault scenarios for a three-shaft turbofan engine, under significant measurement noise to ensure model robustness. Two comparative assessments were also carried out: with a single convolutional-neural-network-architecture-based fault classification method and with a deep long short-term memory-assisted fault detection and isolation method. The results obtained revealed the performance of the proposed method to detect and isolate multiple gas path faults with over 96% accuracy. Moreover, sharing diagnostic tasks with modular architectures is seen as relevant to significantly enhance diagnostic accuracy.

Place, publisher, year, edition, pages
MDPI , 2021. Vol. 9, no 12, article id 337
Keywords [en]
Convolutional neural network, Diagnostics, Fault detection and isolation, Gas turbine, Gradual degradation, Rapid faults
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-56878DOI: 10.3390/machines9120337ISI: 000738099500001Scopus ID: 2-s2.0-85121681459OAI: oai:DiVA.org:mdh-56878DiVA, id: diva2:1626796
Note

Export Date: 12 January 2022; Article; Correspondence Address: Fentaye, A.D.; Future Energy Center, Sweden; email: amare.desalegn.fentaye@mdh.se; Funding details: Stiftelsen för Kunskaps- och Kompetensutveckling, KKS, 20190994; Funding text 1: Funding: This research was funded by the Swedish Knowledge Foundation (KKS) under the project PROGNOSIS, Grant Number 20190994.

Available from: 2022-01-12 Created: 2022-01-12 Last updated: 2023-03-28Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Fentaye, Amare DesalegnZaccaria, ValentinaKyprianidis, Konstantinos

Search in DiVA

By author/editor
Fentaye, Amare DesalegnZaccaria, ValentinaKyprianidis, Konstantinos
By organisation
Future Energy Center
In the same journal
Machines
Energy Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 217 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf