# Validation of Acoustic Emission Waveform Entropy as a Damage Identification Feature
## 1 Introduction
Higher AE activity is generated during yielding of notched specimens (when compared to un-notched specimens) made from nuclear grade austenitic stainless steel, due to the increase in dislocation movement from the plastic zone [15; 16]. Exactly the opposite behaviour was observed in the specimen made from commercial grade austenitic stainless steel, due to reduction of the material in the notch [15]. An increase in AE activity was observed with notch depth during yielding of nuclear grade austenitic stainless steel. This observation was as a result of growth in the plastic zone size with notch depth [16; 24]. With the increase in pre-strain, AE activity during yielding in austenitic stainless steel was shown to decrease [18]. The reduction in AE activity due to an increase in pre-strain was a result of reduced martensite transformation. AE activity during yielding of austenitic stainless steel depends on the level of work hardening [19]. A slight increase of the cold work increases the AE activity, whereas increasing the cold work beyond 20% decreases the AE activity significantly. AE activity during yielding of austenitic stainless steel increased with strain rate [20]. This observation was explained as a result of an increase in source activation. AE activity during yielding of austenitic stainless steel increased significantly at low temperatures (223 K) as a result of accelerated martensite formation [21]. AE has also been successful at determining the onset of yielding in austenitic stainless steel in the supercritical water environment (i.e., 550 \({}^{\circ}\)C and 250 bar) [23]. Apart from steel (e.g., austenitic stainless steel), AE has been used in a number of other heterogeneous materials such as concrete, rocks and masonry [25; 26; 27]. For instance, it has been shown by [PERSON] et al. [25] that the AE technique (i.e., Count) is effective in identifying damages in masonry buildings and concrete frames. An alternative and reliable onset time determination of an AE signal was presented by [PERSON] et al. [26], which was successfully validated in fibre-reinforced concrete beams. AE has also been successful in determining the severity of damage in repaired masonry samples [27]. Despite the reported successful research on AE with austenitic stainless steel, identifying the onset of yielding is challenging in an actual field trial, primarily because the traditional AE monitoring is influenced by a range of user defined settings (e.g., threshold and timings) on the AE data acquisition system. Inaccurate user defined settings can result in an inaccurately classified damage source and can bring difficulty in interpreting the results during yielding [28]. The AE results may also be affected by the noise (e.g., unwanted elastic wave generated due to sloshing in the LNG cargo tank). The latter can be avoided by understanding the nature of noise in a calibration test (i.e., by gathering AE data from the cargo tank during a voyage and analysing the noise content). Once the nature of noise is understood, relevant filtration techniques can be used to eliminate or reduce noise content in the data. In order to rule out the primary reason, the influence of user-defined settings on the AE monitoring has to be minimized. This research presents a new AE feature that is independent of threshold and timing settings. The proposed feature is derived from the idea that each waveform will have a unique disorderness and the measure of this disorderness could be used as an AE feature. The disorderness of a waveform can be extracted from the probability distribution of a waveform, which depends on its discrete voltage values. The number of discrete voltage values in a waveform depends on its window length and the sampling frequency. The damages in LNG cargo containment are likely to occur during rise-time of a typical sloshing impact (i.e., the time taken for the impact to reach the maximum value) [29].