Ship speed power performance under relative wind profiles in relation to sensor fault detection

Abstract

Statistical data analysis and visualization approaches to identify ship speed power performance under relative wind (i.e. apparent wind) profiles are considered in this study. Ship performance and navigation data of a selected vessel are analyzed, where various data anomalies, i.e. sensor related erroneous data conditions, are identified. Those erroneous data conditions are investigated and several approaches to isolate such situations are also presented by considering appropriate data visualization methods. Then, the cleaned data are used to derive various relationships among ship performance and navigation parameters that have been visualized in this study, appropriately. The results show that the wind profiles along ship routes can be used to evaluate vessel performance and navigation conditions by assuming the respective sea states relate to their wind conditions. Hence, the results are useful to derive appropriate mathematical models that represent ship performance and navigation conditions. Such mathematical models can be used for weather routing type applications (i.e. voyage planning), where the respective weather forecast can be used to derive optimal ship routes to improve vessel performance and reduce fuel consumption. This study presents not only an overview of statistical data analysis of ship performance and navigation data but also the respective challenges in data anomalies (i.e. erroneous data intervals and sensor faults) due to onboard sensors and data handling systems. Furthermore, the respective solutions to such challenges in data quality have also been presented by considering data visualization approaches. Keywords: Speed power performance, Data anomaly detection, Sensor fault identification, Weather routing, Statistical data analysis, Ship wind profile