An attempt at applying machine learning in diagnosing marine ship engine turbochargers

• Autorzy: Adamkiewicz Andrzej , Nikończuk Piotr

Identyfikatory

DOI

10.17531/ein.2022.4.19

• Języki publikacji: EN

Abstrakty

EN

The article presents a diagnosis of turbochargers in the supercharging systems of marine engines in terms of maintenance decisions. The efficiency of turbocharger rotating machines was defined. The operating parameters of turbocharging systems used to monitor the correct operation and diagnose turbochargers were identified. A parametric diagnostic test was performed. Relationships between parameters for use in machine learning were selected. Their credibility was confirmed by the results of the parametric test of the turbocharger system and the main engine, verified by the coefficient of determination. A particularly good fit of the describing functions was confirmed. As determinants of the technical condition of a turbocharger, the relationship between the rotational speed of the engine shaft, the turbocharger rotor assembly and the charging air pressure was assumed. In the process of machine learning, relationships were created between the rotational speed of the engine shaft and the boost pressure, and the indicator of the need for maintenance. The accuracy of the maintenance decisions was confirmed by trends in changes in the efficiency of compressors.

Słowa kluczowe

EN

machine learning; compressor diagnosis; marine ship engine; operational decision; neural; network

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2022
• Tom: Vol. 24, no. 4
• Strony: 795--804
• Opis fizyczny: Bibliogr. 33 poz., rys., tab.

Twórcy

autor

Adamkiewicz Andrzej

• Maritime University of Szczecin, Faculty of Mechanical Engineering, ul. Wały Chrobrego 1-2, 70-500 Szczecin, Poland

• https://orcid.org/0000-0002-8314-0660

autor

Nikończuk Piotr

• West Pomeranian University of Technology, Faculty of Maritime Technology and Transport, al. Piastów 17, 70-310 Szczecin, Poland

• https://orcid.org/0000-0002-7809-7653

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).