DigiShip - Digitalisation of ship operations

Platform machineries and sensors onboard the vessel necessary for ship operations are connected to ST Engineering’s NERVA Ship Management System and Sensemaking System (SMS 2 ) to remotely control and monitor platform machineries and sensors from the bridge, or monitored from an operator station on...

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Bibliographic Details
Published in:Journal of physics. Conference series 2022-07, Vol.2311 (1), p.12001
Main Authors: Yue, S P, Chua, T Y, Fan, J W
Format: Article
Language:English
Subjects:
Coastal waters
Cost analysis
Digitization
Downtime
Life cycle costs
Physics
Predictive maintenance
Remote control
Remote sensors
Risk assessment
Sensors
Tugboats
Vessels
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Summary:Platform machineries and sensors onboard the vessel necessary for ship operations are connected to ST Engineering’s NERVA Ship Management System and Sensemaking System (SMS 2 ) to remotely control and monitor platform machineries and sensors from the bridge, or monitored from an operator station on the shore. A case study on remote connection to the shore command centre for a tugboat established using local telco’s 4.5G network with operations in the coastal waters of Singapore is discussed. A systematic approach for security assessment of the NERVA SMS 2 is also discussed. Threat modelling and risk assessment for major systems are done using STRIDE and HEAVENS methodologies respectively. With platform systems and sensors connected to NERVA, sensors data collected can be analyzed via the predictive maintenance function of NERVA SMS 2 to reduce the vessel’s life-cycle cost. NERVA SMS 2 performs advanced analytics on the collected sensors data, providing predictive diagnostics information, which predicts potential machinery failure before it happen, which can greatly reduce unplanned downtime. In addition, there is decision support for maintenance, which are aligned with OEM recommendations, guiding the maintenance operator on part that need to be replaced or serviced based on the analysed symptoms reflected through the data.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2311/1/012001