<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Marine electronically controlled (ME) two-stroke diesel engines occupy the highest market share in newly-built ships and its fuel injection system is quite different and important. Fault diagnosis in the fuel injection system is crucial to ensure the power, economy and emission of ME diesel engines, so we introduce hierarchical multiscale fluctuation dispersion entropy (HMFDE) and a support matrix machine (SMM) to realise it. We also discuss the influence of parameter changes on the entropy calculation’s accuracy and efficiency. The system simulation model is established and verified by Amesim software, and then HMFDE is used to extract a matrix from the features of a high pressure signal in a common rail pipe, under four working conditions. Compared with vectorised HMFDE, the accuracy of fault diagnosis using SMM is nearly 3% higher than that using a support vector machine (SVM). Experiments also show that the proposed method is more accurate and stable when compared with hierarchical multiscale dispersion entropy (HMDE), hierarchical dispersion entropy (HDE), multiscale fluctuation dispersion entropy (MFDE), multiscale dispersion entropy (MDE) and multiscale sample entropy (MSE). Therefore, the proposed method is more suitable for the modelling data. This research provides a new direction for matrix learning applications in fault diagnosis in marine two-stroke diesel engines.</p>
</abstract>

Marine electronically controlled (ME) two-stroke diesel engines occupy the highest market share in newly-built ships and its fuel injection system is quite different and important. Fault diagnosis in the fuel injection system is crucial to ensure the power, economy and emission of ME diesel engines, so we introduce hierarchical multiscale fluctuation dispersion entropy (HMFDE) and a support matrix machine (SMM) to realise it. We also discuss the influence of parameter changes on the entropy calculation’s accuracy and efficiency. The system simulation model is established and verified by Amesim software, and then HMFDE is used to extract a matrix from the features of a high pressure signal in a common rail pipe, under four working conditions. Compared with vectorised HMFDE, the accuracy of fault diagnosis using SMM is nearly 3% higher than that using a support vector machine (SVM). Experiments also show that the proposed method is more accurate and stable when compared with hierarchical multiscale dispersion entropy (HMDE), hierarchical dispersion entropy (HDE), multiscale fluctuation dispersion entropy (MFDE), multiscale dispersion entropy (MDE) and multiscale sample entropy (MSE). Therefore, the proposed method is more suitable for the modelling data. This research provides a new direction for matrix learning applications in fault diagnosis in marine two-stroke diesel engines.

Hierarchical Multiscale Fluctuation Dispersion Entropy for Fuel Injection System Fault Diagnosis

Polish Maritime Research

This work is licensed under the Creative Commons Attribution 4.0 International License.

Marine electronically controlled (ME) two-stroke diesel engines occupy the highest market share in newly-built ships and its fuel injection system is quite...

Hierarchical Multiscale Fluctuation Dispersion Entropy for Fuel Injection System Fault Diagnosis

Data publikacji: 19 kwi 2023

Zakres stron: 98 - 111

DOI: https://doi.org/10.2478/pomr-2023-0010

Słowa kluczowe
Hierarchical multiscale fluctuation dispersion entropy, fuel injection system, support matrix machine, fault diagnosis

© 2023 Qingguo Shi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Hierarchical Multiscale Fluctuation Dispersion Entropy for Fuel Injection System Fault Diagnosis

Data publikacji: 19 kwi 2023

Zakres stron: 98 - 111

DOI: https://doi.org/10.2478/pomr-2023-0010

Słowa kluczoweHierarchical multiscale fluctuation dispersion entropy, fuel injection system, support matrix machine, fault diagnosis

© 2023 Qingguo Shi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Hierarchical multiscale fluctuation dispersion entropy, fuel injection system, support matrix machine, fault diagnosis