How digitalisation will drive ship safety

Mini-Symposium to celebrate Prof Apostolos Papanikolaou How digitalisation will drive ship safety Mini Symposium to honour Prof Apostolos Papanikolaou Pierre C Sames DNV GL 2016 2016-48-PSames SAFER, SMARTER, GREENER

Areas where digitalisation will help improving ship safety Creating new insights from data Combining historic records from multiple data sources to derive ship and fleet insights Casualty data PSC and survey data Environmental and geo data Other ship-related data (Analysing ship movements) Decision-making on-board Aggregating data generated on-board for decision-making Dynamic barrier management Operational support Emergency response Remote assistance (Autonomous ships) 2 DNV GL 2016

Mining casualty data to better understand casualty root causes Navigation 95% Collision 7E-3 5% Struck / Striking Loss of manoeuvrability Extent of damage and survivability depend on whether ship is stricking or being struck Place of incident (at sea, restricted waters, port) Operational State Damage Extent E.g. breach of hull or water ingress: Loss of watertight integrity (LOWI) Probability of water ingress and sinking Safety of crew Survivability Safety PLL: 1.2E-3 20% Weather 5% Equipment 5% Heavy sea 5% Visibility 95% Failure of radar 50% Mooring 50% Narrow fairway/ hydrod. effects Human 70% On board 95% Steering 45% Insufficient instructions by the traffic center 5% ME 55% Consequences Environment Property Spill of bunker oil PLO: 0.3 t Damage to ship (repair, total loss) and cargo Too high speed despite regulations No communication via VHF 10% Insufficient action for collision avoidance, e.g. poor seamanning 10% No proper lookout at the bridge 5% 40% 30% Fatigue or panic Wrong decision by pilot 5% Source: DNV GL FSA on Container Ships update 2015. Based on NTUA database combining IHS, LMIU, GSIS data 3 DNV GL 2016

Exploring PSC and survey data to better understand ship condition ships ships ships 4 DNV GL 2016

Environmental and geo data help understanding loads Combine AIS data and weather information to determine loads Consequences are estimated threshold models how often defined limits were exceeded engineering models combine ship model and sea states to estimate eg fatigue life 5 DNV GL 2016

Other data can be used to complement the safety picture Navigational performance Motion comfort Crew work time Safety culture 6 DNV GL 2016

Barrier management improves our risk understanding Preventative barrier functions Probability reducing Reactive barrier functions Consequence reducing Threat 1 Hazard Consequence 1 Threat 2 Top Event Consequence 2 Threat 3 Consequence 3 Decay Mechanism Decay Mechanism 8 DNV GL 2016

Barrier performance is changing over time 9 DNV GL 2016

Barrier performance is changing over time 10 DNV GL 2016

B.S.1 B.S.2 B.S.3 B.S.4 Dynamic barrier management will be driven by sensors Barrier condition data DBM database Barrier status System Area Hazard Barrier system Containment Detection Status Decision Support - dash board Qualitative Area 1 B.S.1 Risk model Quantitative B.S.2 Escape and evacuation B.S.3 B.S.4 11 DNV GL 2016

Digital twin as base for emergency response and remote assistance 12 DNV GL 2016

But: we first need to build trust in data New areas of certification and standards are opening up Data management standards Data and domain model standards Sensors and sensor systems Wireless networks Data logging systems Data quality Cyber security 13 DNV GL 2016

And we need to model the safety contribution of systems ME ME ME 14 DNV GL 2016

Conclusions and recommendations Conclusions Digital solutions will help driving ship safety in two areas Insights on ship and fleet safety performance On-board decision-making Expect many novel solutions Recommendations Create trust for data-driven solutions Develop a ship functional safety model 15 DNV GL 2016

Thank you for your kind attention. Dr. Pierre C. Sames, Group Technology and Research Director Pierre.Sames@dnvgl.com www.dnvgl.com SAFER, SMARTER, GREENER 16 DNV GL 2016