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
A Review on Deep Learning Applications in Prognostics and Health Management
Department of Industrial Engineering, Dongguan University of Technology, Dongguan, China.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-7458-6820
Department of Management Science, University of Strathclyde, Glasgow, U.K..
Department of Industrial Engineering, Dongguan University of Technology, Dongguan, China.
Show others and affiliations
2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 162415-162438Article, review/survey (Refereed) Published
Abstract [en]

Deep learning has attracted intense interest in Prognostics and Health Management (PHM), because of its enormous representing power, automated feature learning capability and best-in-class performance in solving complex problems. This paper surveys recent advancements in PHM methodologies using deep learning with the aim of identifying research gaps and suggesting further improvements. After a brief introduction to several deep learning models, we review and analyze applications of fault detection, diagnosis and prognosis using deep learning. The survey validates the universal applicability of deep learning to various types of input in PHM, including vibration, imagery, time-series and structured data. It also reveals that deep learning provides a one-fits-all framework for the primary PHM subfields: fault detection uses either reconstruction error or stacks a binary classifier on top of the network to detect anomalies; fault diagnosis typically adds a soft-max layer to perform multi-class classification; prognosis adds a continuous regression layer to predict remaining useful life. The general framework suggests the possibility of transfer learning across PHM applications. The survey reveals some common properties and identifies the research gaps in each PHM subfield. It concludes by summarizing some major challenges and potential opportunities in the domain.

Place, publisher, year, edition, pages
IEEE, 2019. Vol. 7, p. 162415-162438
Keywords [en]
Condition-based maintenance, deep learning, fault detection, fault diagnosis, prognosis
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-76709DOI: 10.1109/ACCESS.2019.2950985ISI: 000497169800116OAI: oai:DiVA.org:ltu-76709DiVA, id: diva2:1370382
Note

Validerad;2019;Nivå 2;2019-11-25 (johcin)

Available from: 2019-11-15 Created: 2019-11-15 Last updated: 2019-12-09Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records BETA

Lin, Jing

Search in DiVA

By author/editor
Lin, Jing
By organisation
Operation, Maintenance and Acoustics
In the same journal
IEEE Access
Other Civil Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 26 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