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.

Abstract.One of the main objectives of Acoustic Emission (AE) monitoring is to identify approaching critical stage of damage in the structure before it fails. State-of-the-art AE analysis is done on the features in both the time and frequency domains. Many features such as centroid frequency, duration, rise-time, count and energy are dependent on acquisition settings; threshold and timing parameters. Incorrect acquisition settings may result in inaccurate classification of the AE source. This work proposes a new feature in the time domain signal based on 2 nd order Renyi's entropy, which proves to be efficient in identifying different stages of damage. Renyi's entropy is a measure of uncertainty or randomness of the signals and is directly derived from the distribution of signal amplitude. Therefore, it is independent of threshold and timing parameters. The validity of the proposed parameter is investigated by performing AE monitoring during fatigue endurance test of 316L stainless steel. Digital Image Correlation (DIC) and global strain monitoring was carried out to relate material damage with AE activity. The result shows Renyi's entropy to be an effective measure to identify critical stages of damage in the material.