Shipping sector is forced to make the most use of digitalisation as it struggles with technical and market challenges during the global coronavirus pandemic, reports Riviera Maritime Media.
Sector goes digitial
Digital performance, standards and internet of things (IoT) connectivity is becoming increasingly important in passenger shipping as operators learn to work remotely.
The sector’s implementation of digitalisation has accelerated as it struggles with technical and market challenges during the global coronavirus pandemic.
Remote working and digital technologies
DNV GL chief executive for maritime Knut Ørbeck-Nilssen says the global Covid-19 crisis has forced shipping companies to invest and implement remote working and digital technologies for business continuation and vessel optimisation.
Period for innovation
This has resulted in the sector entering a renaissance period for innovation. “Digitalisation [has been] turbocharged, accelerating cross-industry collaboration and innovation,” says Mr Knut Ørbeck-Nilssen.
New opportunities
He added new technology has spurred developments in digitalisation creating opportunities for operators, vendors and class.
Remote control & AI in shipping
Drones for inspection
Suppliers of unmanned aerial vehicles and remote-control services have found new markets in ship inspections.
Mr Ørbeck-Nilssen said there are great opportunities with drones for inspection, taking video footage and thickness measurements.
Artificial intelligence
Further, shipping companies and original equipment manufacturers are using artificial intelligence for:
- predictive maintenance,
- intelligent scheduling,
- real-time analytics and
- improving performance.
According to Mr Ørbeck-Nilssen, the mentality has changed and maritime is open to digital ways of working and interaction and there is no doubt automation will continue to be developed on board.
Connectivity and bandwidth
“Fundamental to this is connectivity and bandwidth to facilitate this gradual development in modern vessels.”
Central to IoT and vessel automation are the sensors on board, communicating data to shore, enabling owners to do more diagnostics using AI machine learning for predictive maintenance of equipment.
DNV GL and digitalisation
DNV GL has benefited from digitalisation with higher demand for remote inspections using drones and video cameras. This reduces the need for surveyors to travel to ships for physical surveys.
Digital twin modelling
Digitalisation also enables DNV GL to develop digital twins of ships to manage hull inspection programmes and identify sections of passenger vessels at risk of structural damage.
According to DNV GL principal specialist Gaute Storhaug, Digital twins can help owners, operators and class societies predict the structural performance of ships.
DNV GL period of survey
Inspection regimes for structural damages
“Structural damage can have severe consequences,” he explains, such as hull breakup and sea pollution. “We have inspection regimes to mitigate these risks.” This involves full inspections every five years by class surveyors, but this could be extended to seven years for certain ships.
- For a ship operating in calm environments, the period between class surveys could be extended.
- But for passenger ships operating in harsher environments, this period could be shortened, with inspections focused on the most vulnerable points.
Structural performance
Digital twins of ships could be used to monitor structural performance. DNV GL has an indicator tool to calculate the risk of fatigue cracking and overloading on ships within a fleet.
“This uses machine learning, ship position (AIS) and global wave data, so we can reveal which ships are most at risk among a fleet,” says Mr Storhaug.
A traffic light system is used – with green, orange, and red indicating the most risk – but it has limitations.
On top of this,
- DNV GL created a numerical twin of a ship’s hull structure based on design models which matches ship positions with wave data.
- Engineers can virtually slice ship models to reveal critical details such as stiffeners for inspection.
- Another traffic light system indicates “where you should focus inspections” across the ship’s hull although there are uncertainties such as the wave conditions these structures face in real operations.
DNV GL’s Hybrid twin
To account for this, DNV GL created a hybrid twin.
Working
Mr Storhaug said, “We mix the design model and the numerical twin with sensory information.”
This brings much better accuracy and complete modelling of the structure including transferring loads across the structure.
Sensor positions
By optimising the sensor positions, shipowners can gain more detail of stress on hull structures.
“One of the benefits of the hybrid twin is you can calculate where you should have the sensors,” says Mr Storhaug.
Owners can use this information to reduce operating and compliance costs.
“You can use this for maintenance planning or inspection planning,” says Mr Storhaug. “It can be useful for lifetime extension and troubleshooting. “
Purposes for these different digital twin concepts
There can be many purposes for these different digital twin concepts.
- Predictive maintenance
Digitalisation technologies enable equipment manufacturers to diagnose issues before they become problems on cruise ships and ferries. Rolls-Royce Power Systems is developing predictive prognosis capabilities to diagnose engine problems before they impact operations, says its director for application engineering and automation marine and defence business Kevin Daffey.
To achieve this, Rolls-Royce Power Systems is using machine learning techniques, specifically a neural network trained on “about three months of good engine data” from a testbed with sensors to detect anomalies.
- Corresponding alerts are set for the anomalies.
- If the machine learning model has previously classified the cause of the anomalies, it provides the alert which then triggers action from a remote operating centre and provides advice back to the engine operator.
- When a new anomaly pops up, experts use the data to classify it, aiding the predictive maintenance software’s progress and building a library of potential issues for recognition and diagnosis by the software.
Mr Daffey says:
“That means we can update the models for all the engines and all the operators. Our body of knowledge is continually updated and captured forever, not only for that customer, but for all customers of a particular engine class.”
Holistic diagnostic system
Rolls-Royce Power Systems is using the approach to develop a holistic diagnostic system covering engines, gear boxes, waterjets and other components for a full powertrain health management solution, according to Mr Daffey. This could be linked to the company’s shore-based service for providing advice and intervention.
Ultimately, that information can be combined with external and real-time data on conditions and vessel positioning to offer operational insights to optimise fuel consumption and other critical aspects to help operators make timely decisions.
Mr Daffey says its Artificial Chief Engineer development programme is at “technological readiness level four” and the company hopes it will “become a reality over the next 24 months to enable autonomous technology of the future”.
Sensor packages
These prognosis solutions require packages of sensors. According to VAF Instruments director of research and development Erik van Ballegooijen, a propulsion monitoring system integrates real-time fuel consumption and maritime condition monitoring with cloud storage and analysis applications.
Fuel monitoring sensors
By installing fuel monitoring sensors on ships, owners can replace noon reports with real-time monitoring. “It is good to go for automatic data collection, to show this data on board and to store this information somewhere in the cloud,” he says.
Data should be displayed on board for crew to make immediate performance improvements. It could then be analysed by VAF, a third party or the owner to identify trends across the fleet.
Cloud-stored data
“Once in the cloud, you can do a lot of additional things, like data enrichment, creating key performance indicators and long-term trend visualisation,” says Mr van Ballegooijen.
Shipping companies and technical experts can access cloud-stored data to analyse fuel consumption across fleets of ships and add information from other sources.
“You can get external data, like weather data, or you might also have your own business intelligence tools,” he continues.
Data for charterers analysis
These resources can be connected to propulsion management tools or sent to charterers for analysis. “Exchanging and combining data is valuable for analysis,” says Mr van Ballegooijen. “It is also important to find the quick wins and know which data is immediately relevant.”
Propulsion performance management
For propulsion performance management it is important to have a minimum dataset, including real-time measurements of fuel consumption from flow meters on the fuel supply lines. Other measurements could include speedlogs, GPS and ship draught. External condition information can include wind, wave and current data from onboard sensors or external databases.
This information can be fed into a cloud system such as VAF’s IVY propulsion management solution that uses Microsoft Azure for data cloud storage and applications.
Speed and consumption curve
“With this minimum dataset, you can create ship-speed and fuel consumption curves,” says Mr van Ballegooijen. Shipowners and operators can “create baselines and see improvements or why things are going wrong” to decide how to optimise operations further.
- Digital twin enables waste heat recovery system development
Lappeenranta University of Technology (LUT) is developing a digital twin of a waste-heat recovery system on a cruise ship.
Energy from excess waste
LUT’s digital twin represents the waste-heat recovery system set up in its test centre. This system could be used on cruise ships to produce energy from excess waste and LUT is testing its effectiveness.
3D simulation
The digital twin was generated for an organic Rankine cycle physical unit. Data to create the 3D simulation was collected and transferred to cloud storage using Beckhoff’s technology.
Dynamic model using Simulink
Mr Turunen-Saaresti said this digital twin was based on a dynamic model using Simulink.
“We made multiple pressure and temperature measurements available so we can calibrate our model,” he said. Simulink was used for its suitability for dynamic modelling of thermodynamic processes.
Metadata of system components
LUT’s digital twin required computational resources for online monitoring, predictions and 3D modelling. It also needed capacity to increase the metadata of system components such as heat exchangers and pumps.
“The different components, thermodynamics, fluid dynamics and rotor dynamics – all kinds of behaviours needed to be considered,” said Mr Turunen-Saaresti. “This is why we needed a versatile and dynamic digital twin.”
Main findings from this process
He said that data is crucial for this digital twin and data is obtained from real systems. But, data from ships has its limitations.
“There are limited measurement points and a variety of data acquisition systems installed,” said Mr Turunen-Saaresti.
Challenge with digital twin design
One challenge in developing digital twins from operational data is securing access to the data.
As there are many different stakeholders and companies working in the sector and the ship operators, Mr Turunen-Saaresti questions:
- Who owns the data?
- Who has access to the data?
What is the solution?
A solution is having close co-operation between the various stakeholders to create a dynamic and versatile digital twin that can model complicated systems on ships.
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Source: Riviera Maritime Media
