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Ships and Offshore Structures Volume 17, 2022 - Issue 12
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Articles

Bayesian and machine learning-based fault detection and diagnostics for marine applications

Michail Cheliotisa Maritime Safety Research Centre (MSRC), University of Strathclyde, Glasgow, UKORCID Iconhttps://orcid.org/0000-0002-0056-8494View further author information
ORCID Icon,
Iraklis Lazakisb Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow, UKCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-6130-9410View further author information
ORCID Icon &
Angelos Cheliotisc Starbulk S.A, Athens, GreeceORCID Iconhttps://orcid.org/0000-0003-0103-9284View further author information
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Pages 2686-2698 | Received 17 Dec 2019, Accepted 23 Nov 2021, Published online: 09 Jan 2022

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Read on this site (2)

Çağlar Karatuğ, Yasin Arslanoğlu & C. Guedes Soares. (2023) Review of maintenance strategies for ship machinery systems. Journal of Marine Engineering & Technology 22:5, pages 233-247.
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Ioannis Asimakopoulos, Luis David Avendaño-Valencia, Marie Lützen & Niels Gorm Maly Rytter. (2023) Data-driven condition monitoring of two-stroke marine diesel engine piston rings with machine learning. Ships and Offshore Structures 0:0, pages 1-13.
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Articles from other publishers (2)

Muhittin Orhan & Metin Celik. (2023) A literature review and future research agenda on fault detection and diagnosis studies in marine machinery systems. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, pages 147509022211492.
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Ruihan Wang, Hui Chen & Cong Guan. (2023) DPGCN Model: A Novel Fault Diagnosis Method for Marine Diesel Engines Based on Imbalanced Datasets. IEEE Transactions on Instrumentation and Measurement 72, pages 1-11.
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