Fault diagnosis of me marine diesel engine fuel injector with novel IRCMDE method

• Autorzy: Shi Qingguo , Hu Yihuai , Yan Guohua

Identyfikatory

DOI

10.2478/pomr-2023-0043

• Języki publikacji: EN

Abstrakty

EN

As an important component of the fuel injection system, the fuel injector is crucial for ensuring the power, economy, and emissions for a whole ME (machine electronically-controlled) marine diesel engine. However, injectors are most prone to failures such as reduced pressure at the opening valve, clogged spray holes and worn needle valves, because of the harsh working conditions. The failure characteristics are non-stationary and non-linear. Therefore, to efficiently extract fault features, an improved refined composite multi-scale dispersion entropy (IRCMDE) is proposed, which uses the energy distribution of sampling points as weights for coarse-grained calculation, then fast correlation-based filter (FCBF) and support vector machine (SVM) are used for feature selection and fault classification, respectively. The experimental results from a MAN B&W 6S35ME-B9 marine diesel engine show that the proposed algorithm can achieve 92.12% fault accuracy for injector faults, which is higher than multiscale dispersion entropy (MDE), refined composite multiscale dispersion entropy (RCMDE) and multiscale permutation entropy (MPE). Moreover, the experiment has also proved that, due to the double-walled structure of the high-pressure fuel pipe, the fuel injection pressure signal is more accurate than the vibration signal in reflecting the injector operating conditions.

Słowa kluczowe

EN

marine diesel engine; fuel injector; improved refined composite multi-scale dispersion entropy; fault diagnosis

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2023
• Tom: nr 3
• Strony: 96--110
• Opis fizyczny: Bibliogr. 35 poz., rys.

Twórcy

autor

Shi Qingguo

• Shanghai Maritime University, Shanghai, China

• https://orcid.org/0000-0003-3776-8438

autor

Hu Yihuai

• Shanghai Maritime University, Shanghai, China

autor

Yan Guohua

• Shanghai Maritime University, Shanghai, China

• https://orcid.org/0000-0002-1065-5452

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).