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Towards Artificial Intelligent Maintenance System (AIMS) via Predictive Failure Modelling and Numerical simulation.


Towards Artificial Intelligent Maintenance System (AIMS) via Predictive Failure Modelling and Numerical simulation.




  • Ongoing


Project website

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Jul 2020 - Jul 2023

Type of action

Joint Call

Project Abstract

Safety of marine constructions is fundamentally dependent on corrosion control by protective coatings and their maintenance. Coating maintenance is expensive and has safety challenges. To determine the optimal time to perform coating maintenance is difficult since several issues must be considered at the same time, like economy, logistics, safety without compromising the integrity of the construction or the safety workers. Intelligent decision support systems (DSS) will aid the maintenance optimization. The introduction of sensor- and drone photo monitoring enables intelligent predictive maintenance systems and decision support systems also for coatings. This process can become even more effective if predictive simulations can forecast coating degradation and corrosion induced damage. Sensors and automatic inspections are now being introduced and deliver (computationally) useful information. Key to the success of this revolution is that information can be extracted from the data. Developing sensors, computational models, software tools and AI for this purpose is the technical objective of this project.

The project consists of four main technical developments:

  • Laboratory investigation of correlation between environmental parameters and coating degradation to fill the holes in our current understanding
  • Development of acoustic sensors for monitoring coating degradation. Inspection of atmospheric coatings is achieved by photo, aided by drones, which is known technology
  • Development of a neural network (AI) for analysing sensor data, picture data and simulation data on coating degradation
  • Development of a model to predict the coating degradation rate and corrosion from the current state, and future need for coating maintenance

TAIFUN will start at TRL4 with the goal to achieve TRL6 where the modelling approach will be tested, calibrated, verified and validated on damage scenarios defined by industrial partners.

The general objectiveof the project is to support safety of marine steel constructions and workforce in a cost effective way by making better decisions about coating maintenance. Emphasis is on implementing the smart digital approach into decision making. The project also aims to create a stimulating an interdisciplinary partnership, with actors from the research institutions and private sector across borders, promoting the exchange of ideas, methods, techniques as well as enabling an accelerated technology transfer from science to industrial scale and a continuous collaboration between the partners. The suggested program addresses a series of interlinking technologies and fundamental science,with the objective to improve safety and economic competitiveness of the European maritime industry.

TAIFUN is funded by the MarTERA partners Research Council of Norway (RCN), German Federal Ministry for Economic Affairs and Energy (BMWi) and Flanders Innovation & Entrepreneurship (VLAIO).



Dr Ole Øystein Knudsen, SINTEF Industry, Norway



SINTEF Industry, Research institute, Norway

Helmholtz-Zentrum Hereon, Research institute, Germany

Jotun, Large scale enterprise, Norway

Develogic GmbH, SME, Germany

Zensor, SME, Belgium