The recent dramatic fluctuations in oil and gas prices are forcing operators to look at radically new ways of maintaining the integrity of their structures. Moreover, the life of old structures has to be extended. This includes the replacement of expensive periodic in-service inspections with cost-efficient structural health monitoring (SHM) with permanently installed sensors. Mooring chains for floating offshore installations, typically designed for a 25-year service life, are loaded in fatigue in a seawater environment. There is no industry consensus on failure mechanisms or even defect initiation that mooring chains may incur. Moorings are safety-critical areas, which by their nature are hazardous to inspect. Close visual inspection in the turret is usually too hazardous for divers, yet is not possible with remotely operated vehicles (ROVs), because of limited access. Conventional non-destructive techniques (NDTs) are used to carry out inspections of mooring chains in the turret of floating production storage and offloading (FPSO) units. Although successful at detecting and assessing the fatigue cracks, the hazardous nature of the operation calls for remote techniques that can be applied continuously to identify damage initiation and progress. Appropriate replacement plans must enhance current strategies by implementing real-time data retrofit.

Acoustic emission (AE) is a passive nondestructive testing (NDT) technique which is employed to identify critical damage in structures before failure can occur. Currently, AE monitoring is carried out by calculating the features of the signal received by the AE sensor. User-defined acquisition settings (i.e., timing and threshold) significantly affect many traditional AE features such as count, energy, centroid frequency, rise time and duration. In AE monitoring, AE features are strongly related to the damage sources. Therefore, AE features that are calculated due to inaccurate user-defined acquisition settings can result in inaccurately classified damage sources. This work presents a new feature of the signal based on the measure of randomness calculated using second-order Renyi's entropy. The new feature is computed from its discrete amplitude distribution making it independent of acquisition settings. This can reduce the need for human judgement in measuring the feature of the signal. To investigate the effectiveness of the presented feature, fatigue testing is conducted on an un-notched steel sample with simultaneous AE monitoring. Digital image correlation (DIC) is measured alongside AE monitoring to correlate both monitoring methods with material damage. The results suggest that the new feature is sensitive in identifying critical damages in the material.

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