A study on the key performance indicator of the dynamic positioning system
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The dynamic positioning system (DPS) maintains an offshore vessel's position and heading under various environmental conditions by using its own thrust. DPS is regarded as one of the most important systems in offshore vessels. So, efficient operation and maintenance of the DPS are important issues. To monitor the DPS, it is necessary to define an appropriate key performance indicator (KPI) that can express the condition of the DPS from the perspective of operational efficiency and maintenance. In this study, a new KPI for the DPS is proposed considering the efficiency of the machinery and controller, the energy efficiency, and the environmental conditions in which the DPS is operated. The KPI is defined as a function of control deviation, energy consumption, and environmental load. A normalization factor is used to normalize the effect of environmental load on the KPI. The KPI value is calculated from DPS simulation and model test data. The possibility of applying the KPI to monitoring of DPS condition is discussed by comparing the values. The result indicates the feasibility of the new KPI.
Abstract
The dynamic positioning system (DPS) maintains an offshore vessel's position and heading under various environmental conditions by using its own thrust. DPS is regarded as one of the most important systems in offshore vessels. So, efficient operation and maintenance of the DPS are important issues. To monitor the DPS, it is necessary to define an appropriate key performance indicator (KPI) that can express the condition of the DPS from the perspective of operational efficiency and maintenance. In this study, a new KPI for the DPS is proposed considering the efficiency of the machinery and controller, the energy efficiency, and the environmental conditions in which the DPS is operated. The KPI is defined as a function of control deviation, energy consumption, and environmental load. A normalization factor is used to normalize the effect of environmental load on the KPI. The KPI value is calculated from DPS simulation and model test data. The possibility of applying the KPI to monitoring of DPS condition is discussed by comparing the values. The result indicates the feasibility of the new KPI.
참고문헌 (10)
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