Application of clustering techniques for visually evoked potentials based detection of vision impairments

Identification of fatigue damage evaluation using entropy of acoustic emission waveform

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.