MAROFF KPN: Digital Twins for Vessel Life Cycle Service (TwinShip) (2018-2021)

The project is supported by Knowledge-building Project for Industry (KPN)

Project partners

  • NTNU in Ålesund
  • Kongsberg Maritime AS (before called Rolls-Royce Marine AS)
  • DNV-GL
  • SINTEF Ålesund
  • Ålesund Kunnskapspark AS

Project Description

There is a strong demand for innovation and efficiency within marine system design, operations, and life cycle service. Norwegian maritime industrial cluster is a world leader in developing complex, customized ships and offshore vessels to the global market, particularly for demanding operations, where safety and environment are focused areas.

Nowadays, modern marine vessels operate increasingly autonomous through strongly interacting subsystems [2]. These systems are dedicated to a specific, primary objective of the vessel or may be part of the general essential ship operations. Between sub-systems, they exchange data and make coordinated operational decisions, ideally without any user interaction. Designing, operating and life cycle service supporting such vessels is a complex and intricated engineering task requiring an efficient development approach to consider the mutual interaction between subsystems and the inherent multi-disciplinarily. Scalable simulation technologies should take the lead in this process. Furthermore, the work flow in maritime industry does not stop after vessel delivery. Through system updating or due to life cycle maintenance, subsystems can change. The overall behaviour of the entire vessel still needs to be efficient and value robust. To make sure of that, product design and product use need to be coupled already during early stages of design, which requires traceability through a performance data management system that spans the entire vessel lifecycle.

The recent years have seen an increasing interest in developing and employing digital twins, big data and cloud computing for maritime industrial system design, ship intelligence, and operational service. Digitalization has become a key aspect of making the maritime industries more innovative, efficient and fit for future operations. Increased use of advanced tools for designing and evaluating system performance, safety and structural integrity are generating a range of digital models of a vessel and its equipment. In the operational phase, cheaper sensors and increased connectivity together with increasing data storage and computational power enable for new ways of managing a vessel’s safety and performance.

The goal of this research is to develop digital twins of maritime systems and operations, which is an open virtual simulator as the next generation of marine industrial infrastructure not only for overall system design, allowing configuration of systems and verification of operational performance, but also more focusing to provide early warning, life cycle service support, and system behaviour prediction (Figure 1).

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Figure 1. The development of digital twins system for marine industry

Today’s maritime engineering systems are operating in highly dynamic environments. The challenge is to develop a concept leveraging on the different levels of system specific services already provided by manufacturers, where safety and efficient performance of complex integrated systems can be managed from the early stages of a new build vessel project and throughout the vessel’s life-cycle. In order to be able to respond fast to unexpected events and anomalies, future systems need to become more autonomous. Advanced marine systems should be able to decide between different actions, adapt to dynamic environments and execute high-level task specifications without explicitly being programmed. To meet requirements, they will need access to very realistic models of the current state of the process, and in addition, their own behavior in interaction with the environment in the real world. From a simulation point of view, digital twins are the next wave in modeling, simulation and optimization technology. It will be a significant scientific and operational achievement for the maritime industry, keeping Norway on the technological lead.

The marine digital twin tracks information on all parameters to define how each individual module and sub modules behaves over its entire useful life, including the initial design and further refinement, manufacturing related deviations, modifications, uncertainties, updates as well as sensor data from on-board systems, maintenance history and all available historical and fleet data obtained using data mining. Using digital twins for marine engineering could bring the following advantages.

Working packages

  • WP1 – Develop an open digital twins platform for marine design, operation, and maintenance.
  • WP2 – Development tools for early warning, prediction, and optimization based on digital twins for maritime industry.
  • WP3 – Demonstrators – Subsystem and operational verification process.

Workshops

  • KPN Twinship Kick-Off Meeting, 20 June 2018, NMK II.
  • KPN Twinship WP2 workshop, 13 December 2018, NTNU i Ålesund.

Publications

  1. Lars Ivar Hatledal, Yingguang Chu, Arne Styve, and Houxiang Zhang: Vico: An Entity-Component-System Based Co-simulation Framework, Simulation Modelling Practice and Theory, accepted, 2020.
  2. Lars Ivar Hatledal, Robert Skulstad, Guoyuan Li, Arne Styve, and Houxiang Zhang: Co-simulation as a Fundamental Technology for Twin Ships, MIC Journal Modeling, Identification and Control, vol. 41, no. 4, pp. 297-311, 2020.
  3. Luman Zhao, Guoyuan Li, and Houxiang Zhang: Multi-ship collision avoidance control strategy in close-quarters situations: a case study of Dover Strait ferry maneuvering, 2020-46th Annual Conference of the IEEE Industrial Electronics Society (IECON), Singapore, pp. 303-309, October 18-21, 2020.
  4. Peihua Han, Guoyuan Li, Robert Skulstad, Stian Skjong, and Houxiang Zhang: A Deep Learning Approach to Detect and Isolate Thruster Failures for Dynamically Positioned Vessels Using Motion, IEEE Transactions on Instrumentation and Measurement, DOI:10.1109/TIM.2020.3016413, 2020.
  5. Robert Skulstad, Guoyuan Li, Thor Inge Fossen, Bjørnar Vik, and Houxiang Zhang: A Hybrid Approach to Motion Prediction for Ship Docking — Integration of a Neural Network Model into the Ship Dynamic Model, IEEE Transactions on Instrumentation and Measurement, DOI: 10.1109/TIM.2020.3018568, 2020.
  6. Xu Cheng, Guoyuan Li, André Listou Ellefsen, Shengyong Chen, Hans Petter Hildre, and Houxiang Zhang: A novel densely connected convolutional neural network for sea state estimation using ship motion data, IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 9, pp. 5984-5993, DOI: 10.1109/TIM.2020.2967115, 2020.
  7. Tongtong Wang, Guoyuan Li, Robert Skulstad, Vilmar Æsøy, and Houxiang Zhang: An effective model-based thruster failure detection method for dynamically positioned ships, IEEE International Conference on Mechatronics and Automation (ICMA), Beijing, China, pp. 898-904, October 13-16, 2020.
  8. Luman Zhao, Guoyuan Li, Knut ReMøy, Baiheng Wu, and Houxiang Zhang: Development of onboard decision supporting system for ship docking operations, in 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), Kristiansand, Norway, pp. 1456-1462, November 9-13, 2020.
  9. Baiheng Wu, Guoyuan Li, Luman Zhao, Hans Petter Hildre, and Houxiang Zhang: A human-expertise based statistical method for analysis of log data from a commuter ferry, in 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), Kristiansand, Norway, pp. 1471-1477, November 9-13, 2020.
  10. Yingguang Chu, Guoyuan Li, and Houxiang Zhang: Incorporation of ship motion prediction into active heave compensation for offshore crane operation, in 15th IEEE Conference on Industrial Electronics and Applications (ICIEA), Kristiansand, Norway, pp. 1444-1449, November 9-13, 2020.
  11. André Listou Ellefsen, Peihua Han, Xu Cheng, F. T. Holmeset, V. Æsøy, and Houxiang Zhang: Online Fault Detection in Autonomous Ferries: Using fault-type in-dependent spectral anomaly detection’, IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 10, pp. 8216–8225, 2020, DOI: 10.1109/TIM.2020.2994012
  12. Yonghui Shuai, Guoyuan Li, Jinshan Xu, and Houxiang Zhang: An effective ship control strategy for collision-free maneuver toward a dock, IEEE Access, vol. 8, pp. 110140-110152, DOI: 10.1109/ACCESS.2020.3001976.
  13. Lars Ivar Hatledal, Frederic Collonval, and Houxiang Zhang: Enabling Python Driven Co-Simulation Models with PythonFMU, ECMS 2020 Proceedings, Communications of the ECMS , Volume 34, Issue 1, June 2020, DOI: 10.7148/2020-0235
  14. Xu Cheng, Guoyuan Li, Robert Skulstad, Shenyong Chen, Hans Petter Hildre and Houxiang Zhang: Modeling and analysis of motion data from dynamically positioned vessels for sea state estimation, IEEE International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, pp. 6644-6650, 20–24 May, 2019.
  15. Xu Cheng, Guoyuan Li, Robert Skulstad, Shengyong Chen, Hans Petter Hildre and Houxiang Zhang: Data-driven uncertainty and sensitivity analysis for ship motion modeling in offshore operations, Ocean Engineering, vol. 179, pp. 261-272, 2019, DOI: 10.1016/j.oceaneng.2019.03.014.
  16. Xu Cheng, Guoyuan Li, Robert Skulstad, Shengyong Chen, Hans Petter Hildre and Houxiang Zhang: A neural network based sensitivity analysis approach for data-driven modeling of ship motion, IEEE Journal of Oceanic Engineering, 2018, DOI: 10.1109/JOE.2018.2882276.
  17. Robert Skulstad, Guoyuan Li, Houxiang Zhang, and Thor I. Fossen. “A Neural Network Approach to Control Allocation of Ships for Dynamic Positioning”. IFAC-PapersOnLine 51, no. 29, pp. 128-133, 2018.
  18. Robert Skulstad, Guoyuan Li, Thor I. Fossen, Bjørnar Vik and Houxiang Zhang: Dead reckoning of dynamically positioned ships: using an efficient recurrent neural network, IEEE Robotics & Automation Magazine, vol. 26, no. 3, pp. 39-51, 2019, DOI: 10.1109/MRA.2019.2918125.
  19. Guoyuan Li, Hans Petter Hildre and Houxiang Zhang: Toward time-optimal trajectory planning for autonomous ship maneuvering in close-range encounters, IEEE Journal of Oceanic Engineering, 2019, DOI: 10.1109/JOE.2019.2926822.
  20. André Listou Ellefsen, Emil Bjørlykhaug, Vilmar Æsøy, Sergey Ushakov, and Houxiang Zhang. “Remaining Useful Life Predictions for Turbofan Engine Degradation Using Semi-Supervised Deep Architecture”. Reliability Engineering & System Safety, accepted, 2018.