Empirical Approach to Defect Detection Probability by Acoustic Emission Testing

Abstract

Estimation of probability of defect detection (POD) is one of the most important problems in acoustic emission (AE) testing. It is caused by the influence of the material microstructure parameters on the diagnostic data, variability of noises, the ambiguous assessment of the materials emissivity, and other factors, which hamper modeling the AE data, as well as the a priori determination of the diagnostic parameters necessary for calculating POD. In this study, we propose an empirical approach based on the generalization of the experimental AE data acquired under mechanical testing of samples to a priori estimation of the AE signals emitted by the defect. We have studied the samples of common industrial steels 09G2S (similar to steel ANSI A 516-55) and 45 (similar to steel 1045) with fatigue cracks grown in laboratory conditions during cyclic testing. Empirical generalization of data using probabilistic models enables estimating the conditional probability of record emissivity and amplitudes of AE signals. This approach allows to eliminate the existing methodological gap and to build a comprehensive method for assessing the probability of fatigue cracks detection by the AE testing.

Combined Acoustic Emission and Digital Image Correlation for Early Detection and Measurement of Fatigue Cracks in Rails and Train Parts under Dynamic Loading

“…This was rather coarse for accurate measurements, but was enough for navigating a fine tool with higher accuracy to the area of the crack for detailed metrological inspection. To identify the moment T AE of crack appearance, the threshold criterion [ 41 ] was applied. For each new AE event, we evaluated its position and updated the location histogram.…”

Study on the mechanical properties and acoustic emission signal characteristics of freezing pipe

“…It enables regular equipment inspections and online monitoring. AE technology is increasingly recognized as an irreplaceable research method and testing means in certain domains (Prokop'ev et al, 2023;Banerjee and Mukherjee, 2023;Barat et al, 2021;Sohaib et al, 2019).…”