Floating offshore production units are typically anchored to the seabed with mooring chains. Structural degradation of mooring chain steel can lead to premature failure with large environmental and financial consequences. In the oceanic environment, corrosion and fatigue are the dominant failure modes for mooring chains. These damage mechanisms are hard to predict and even harder to identify at early stages of damage evolution. Continuous research to develop improved monitoring techniques is performed to increase the safety and reliability of these structures. However, inspection techniques that can accurately and quantitatively diagnose these damage mechanisms in the submerged steel parts are yet insufficient.

Acoustic emission (AE) monitoring is a non-destructive evaluation technique that can identify structural degradation in solid materials. The technique consists of recording and processing the ultrasonic waves generated by irreversible changes in the material matrix due to damage growth. This research is focused on the global parametric analysis of the primary features of AE signals recorded during corrosion-fatigue experiments performed with submerged steel and non-contact sensors. Three experiments have been analysed to assess which of the AE primary features can most accurately correlate to the fatigue damage evolution.

Signal characterization based on their energy and duration was performed to distinguish three levels of severity. Based on a global parametric analysis the hit-rate and energy-rate have the highest potential and show good correlation to the corrosion-fatigue damage evolution. However, the global features seem to differ notably between different samples and several moments with absence of recorded activity precede final failure. Performing assessment only based on the global features is not considered sufficient. Thus, local analysis methods for AE signals is suggested to extract further information on the source of the emitting damage mechanisms.