Passenger ship operators can use IoT to monitor vessel performance and machinery condition, while digital twins model hull structures
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.
In the short term, demand for cruises and ferry travel has plummeted, but in the longer term it will return, and ships will have more automation with higher levels of remote technology. And passenger ship operators will have more real-time monitoring, diagnostics, software and analytics for remote decision support.
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.
“Ultimately, we want to move to where we can provide condition-based asset management based on how an engine actually is operated,” he says.
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 and 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 which 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.
“That means we can update the models for all the engines and all the operators,” Mr Daffey says. “Our body of knowledge is continually updated and captured forever, not only for that particular customer, but for all customers of a particular engine class,” he says.
Rolls-Royce Power Systems is using this 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 to provide advice and intervention.
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 to help operators make timely decisions.
Mr Daffey says his company sees the combination of all of these data-driven elements as enablers for remote operations where decisions are taken from shore, leading eventually to autonomous operations where ships (or the software that powers and monitors them) “take decisions themselves”.
Mr Daffey says this 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 the autonomous technology of the future”.
Digital twin modelling
Digitalisation is also enabling class societies to develop digital twins of ships to manage hull inspection programmes and identify sections of passenger vessels at risk of structural damage.
Digital twins can help owners, operators and class societies predict the structural performance of ships, says DNV GL principal specialist Gaute Storhaug. “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.
“We want to have an inspection regime that relates to the risk of your vessel,” Mr Storhaug says. 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 – of green, orange and red indicating the most at 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 and matches ship positions with wave data. “We mix the design model and the numerical twin with sensory information,” says Mr Storhaug. “This brings much better accuracy and complete modelling of the structure” including transferring loads across the structure.
Lappeenranta University of Technology (LUT) is developing a digital twin of a waste-heat recovery system on a cruise ship. LUT associate professor Teemu Turunen-Saaresti says this digital twin represents the waste-heat recovery system set up in LUT’s test centre. This system could be used on cruise ships to produce energy from excess waste and LUT is testing its effectiveness.
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.
Mr Turunen-Saaresti says 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 thermodynamic processes.
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,” he says. “This is why we needed a versatile and dynamic digital twin.”
LUT needed to get data from real systems to develop this model. “Data is crucial for this digital twin,” says Mr Turunen-Saaresti. But data from ships has its limitations. “There are limited measurement points and a variety of data acquisition systems installed,” he continues. One challenge in developing digital twins from operational data is securing access to the data. “Ownership of the data or who has access to the data is not clear,” he says.
A solution is having close co-operation between stakeholders to create a dynamic and versatile digital twin that can model complicated systems on ships.
Prognosis solutions require packages of sensors
A propulsion monitoring system integrates real-time fuel consumption and maritime condition monitoring with cloud storage and analysis applications, according to VAF Instruments director of research and development Erik van Ballegooijen.
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 and important to show this data on board,” he says. “This data should also be sent to shore and stored in the cloud.”
Displaying the data on board allows 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.
“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.
Riviera is hosting a week of free to attend 45-minute webinars focused on passenger shipping commencing 21 September. Register your interest now