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

Funding programme
MarTERA Call 2019
Project start
Project end
Total budget
1.080.000 €
● SINTEF Industry (NO)
● HSU (DE)
● Jotun AS (NO)
● develogic GmbH (DE)
● Zensor NV (BE)

Monitoring coating maintenance time with sensors and artificial intelligence is an ongoing demand in the Industry 4.0 approach, particularly for maritime industry, where accessibility to steel structures is too challenging. Despite the accessibility of marine steel structures, furthured parameters may play role lonely or simultanteasly on corrosion initiation and corrosion progress phases. Some of these parameteres like temperature, humadity, salinity and time of wetness can be directly collected through site data collection phase, whereas measurement and influence of some of them like inspection methods, inspection method accuracy, accediental issues and human errors are not so far clear. Therefore, developing a systematic and predictive model considering all these issues are highly required.

In this regard, two type of data analysis approach are separately considered: (I) Analysis of the site collected data and available marine atmospheric databases in the literature by fast analysis methods like machine learning approach. Due to the possible limitatin of site or database data in data analysis, nummerical modeling of corrosion with all these parameters was also considered in this project. (II) Effective analysis of the image data collected from the site and the existing databases provided by industrial partners by fast and inteligent machine learning approaches. A sensor is then going to be designed and developed based on above data analysis to measture and predict the optimum coatting maintannace. The project has been designed in 2 TRLs nad 6 working packages as explained in the abstract figure below:

Figure Taifun


Dr. Bahman Daneshian
Dr. Bahman Daneshian

Department of Interface Modelling

Phone: +49 4152 87 1928

E-mail contact