Bicharacteristic Probability of Detection of Crack under Multi-factor Influences using alternating current field measurement technique

Abstract

Alternating Current Field Measurement (ACFM) technique has been widely used in various fields such as oil & gas, aerospace areas. However, during the detection process, the probability of detection (POD) is influenced by multi-factors such as lift-off height, scanning angle, and detection speed. In this paper, a bicharacteristic probability of detection (BPOD) model is developed for quantifying the crack detection reliability using integrated Bx and Bz signals in ACFM technique. A Multidimensional Bicharacteristic Probability of Detection model is established to study the influence of multiple factors on the BPOD of crack. A Bayesian network-based method is proposed to reverse the hierarchical impact weights of influence factors on the BPOD of ACFM detection process. The results show that the BPOD model considering both Bx and Bz response signals can evaluate smaller defects than the POD model. The multiple influencing factors can substantially decrease the BPOD of cracks. Meanwhile, cracks tend to exhibit larger dimensions under equal BPOD subjected to the multiple influence factors. The hierarchical impact weights on BPOD of defects are ranked as follows: lift-off height, detection speed, personnel involved in the detection process, and scanning angle. This approach can retrospectively find the potential contributors to the missed detections based on the BPOD.