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. Duration of the project is 3 years.
Project leader: Ole Øystein Knudsen
Category: Teknisk-industrielle institutter
Institution: SINTEF AS