Probabilistic analysis of electromagnetic acoustic resonance signals for the detection of pipe wall thinning

Autor: Noritaka Yusa, Tetsuya Uchimoto, Makoto Moroi, Daiki Iwata, Toshiyuki Takagi, Haicheng Song
Přispěvatelé: Graduate School of Engineering, Tohoku University, Institute of Fluid Sciences [Sendai] (IFS), Tohoku University [Sendai], ELyTMaX, École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Tohoku University [Sendai]-Centre National de la Recherche Scientifique (CNRS)
Rok vydání: 2019
Předmět:
Zdroj: Nondestructive Testing and Evaluation
Nondestructive Testing and Evaluation, Taylor & Francis, 2019, pp.1-16. ⟨10.1080/10589759.2019.1679141⟩
ISSN: 1477-2671
1058-9759
Popis: International audience; This study proposes a probability of detection (POD) model for the probabilistic analysis of the detectability of electromagnetic acoustic resonance (EMAR) method for the detection and evaluation of pipe wall thinning. Forty-one carbon steel plate samples with an artificially corroded groove were prepared to simulate pipe wall thinning caused by flow-assisted corrosion. Experiments were performed to gather EMAR signals from the samples, and subsequently the depths of the grooves were evaluated based on the fundamental frequency of the measured signals. The results of the experiments showed that the error in evaluating the depth of a groove tended to increase with the depth. The results also confirmed that the surface roughness of the groove would contribute to the error, and the thickness of a plate without corrosion can be quite accurately evaluated. Analyzing the measured EMAR signals using the proposed POD model, which takes these characteristics into consideration, and a conventional one confirmed that the proposed model can more reasonably evaluate the probability of detection against small wall thinning, as well as the false positive rate.
Databáze: OpenAIRE