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

<p>Under the recent background of ‘Green Shipping’ and rising fuel prices, it is very important to reduce the fuel consumption rate of ships, which is directly affected by the performance of the main engine. A reasonable maintenance schedule can optimise the performance of the main engine. However, a traditional maintenance schedule is based on the navigation distance and time, ignoring many other factors, such as a harsh working environments and frequently changing operating conditions, which will lead to faster performance degradation. In this study, a real-time evaluation method combing big data of ship energy efficiency with physics-based analysis is proposed to judge the degradation of main engine performance and assist in determining the maintenance schedule. Firstly, based on the developed ship energy efficiency big data platform, the distribution statistics and comparison of different operating states are carried out. Gaussian mixture model (GMM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) are used to cluster the data and the high-density data areas are obtained as the analysis points. Then, the data of the analysis points are polynomial fitted, by the least square method, to obtain the propulsion characteristics curves, load characteristic curves, and speed characteristic curves, which can be used to observe the performance degradation of the main engine. The results show that this method can effectively monitor the degradation degree of the main engine performance, and is of great significance to fuel efficiency improvements and greenhouse gas (GHG) emissions reduction.</p>
</abstract>

Under the recent background of ‘Green Shipping’ and rising fuel prices, it is very important to reduce the fuel consumption rate of ships, which is directly affected by the performance of the main engine. A reasonable maintenance schedule can optimise the performance of the main engine. However, a traditional maintenance schedule is based on the navigation distance and time, ignoring many other factors, such as a harsh working environments and frequently changing operating conditions, which will lead to faster performance degradation. In this study, a real-time evaluation method combing big data of ship energy efficiency with physics-based analysis is proposed to judge the degradation of main engine performance and assist in determining the maintenance schedule. Firstly, based on the developed ship energy efficiency big data platform, the distribution statistics and comparison of different operating states are carried out. Gaussian mixture model (GMM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) are used to cluster the data and the high-density data areas are obtained as the analysis points. Then, the data of the analysis points are polynomial fitted, by the least square method, to obtain the propulsion characteristics curves, load characteristic curves, and speed characteristic curves, which can be used to observe the performance degradation of the main engine. The results show that this method can effectively monitor the degradation degree of the main engine performance, and is of great significance to fuel efficiency improvements and greenhouse gas (GHG) emissions reduction.

Monitoring the Performance of a Ship’s Main Engine Based on Big Data Technology

Polish Maritime Research

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

Under the recent background of ‘Green Shipping’ and rising fuel prices, it is very important to reduce the fuel consumption rate of ships, which is directly...

Monitoring the Performance of a Ship’s Main Engine Based on Big Data Technology

Publicado en línea: 29 oct 2022

Páginas: 128 - 140

DOI: https://doi.org/10.2478/pomr-2022-0033

Palabras clave
Big data of ship energy efficiency, Main engine, Performance evaluation, Cluster analysis, Mechanism analysis

© 2022 Meng Liang et al., published by Sciendo

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

Monitoring the Performance of a Ship’s Main Engine Based on Big Data Technology

Publicado en línea: 29 oct 2022

Páginas: 128 - 140

DOI: https://doi.org/10.2478/pomr-2022-0033

Palabras claveBig data of ship energy efficiency, Main engine, Performance evaluation, Cluster analysis, Mechanism analysis

© 2022 Meng Liang et al., published by Sciendo

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

Palabras clave
Big data of ship energy efficiency, Main engine, Performance evaluation, Cluster analysis, Mechanism analysis