Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review-Reference-Cited by-同舟云学术

Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review

Published:2022-03-04 Issue:12 Volume:52 Page:14246-14280
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Fernandes Marta^ORCID,Corchado Juan Manuel,Marreiros Goreti

Funder

fundação para a ciência e a tecnologia

agência nacional de inovação

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-022-03344-3.pdf

Reference98 articles.

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2. Holmberg K, Adgar A, Arnaiz A, Jantunen E, Mascolo J (2010). In: Holmberg K, Adgar A, Arnaiz A, Jantunen E, Mascolo J, Mekid S (eds) E-maintenance. Springer, London

3. Muller A, Crespo Marquez A, Iung B (2008) On the concept of e-maintenance: Review and current research. Reliab Eng Syst Saf 93(8):1165–1187. https://doi.org/10.1016/j.ress.2007.08.006

4. Mobley R K (2001) Predictive Maintenance. In: Plant Engineer’s Handbook. Elsevier, pp 867–888

5. Sullivan G, Pugh R, Melendez A P, Hunt WD (2010) Operations & maintenance best practices-a guide to achieving operational efficiency (release 3). Technical Report, Pacific Northwest National Lab.(PNNL), Richland

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