Visual Analytics in Ship Performance and Navigation Information for Sensor Specific Fault Detection

Abstract

Ocean internet of things (IoT - onboard and onshore) collects big data sets of ship performance and navigation information under various data handling processes. That extract vessel performance and navigation information that are used for ship energy efficiency and emission control applications. However, the quality of ship performance and navigation data can play an important role in such applications, where sensor faults may introduce various erroneous data regions and that may degrade to the outcome. This study proposes visual analytics, where hidden data patterns, clusters, correlations and other useful information are visually from the respective data set extracted, to identify such erroneous data regions. The domain knowledge (i.e. ship performance and navigation conditions) has also been used to interpret such erroneous data regions and identify the respective sensors that relate to the same situations. Finally, a ship performance and navigation data set of a selected vessel is analyzed to identify erroneous data regions for three selected sensor fault situations (i.e. wind, log speed and draft sensors) under the proposed visual analytics. Hence, this approach can be categorized as a sensor specific fault detection methodology by considering the same results.