Experimental verification of improved probability of detection model considering the effect of sensor’s location on low frequency electromagnetic monitoring signals
Issue title: Selected papers from the International Symposium on Applied Electromagnetics and Mechanics - ISEM 2019
Affiliations: [a] Department of Quantum Science and Energy Engineering, Graduate School of Engineering, Tohoku University, Sendai, Miyagi, Japan
Correspondence:
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Corresponding author: Haicheng Song, Department of Quantum Science and Energy Engineering, Graduate School of Engineering, Tohoku University, 6-6-01-2, Aramaki Aza Aoba, Aoba-ku, Sendai, Miyagi, Japan. E-mail: song.haicheng.p3@dc.tohoku.ac.jp
Abstract: Structural health monitoring (SHM) is a promising method for maintaining the integrity of structures. A reasonable approach is necessary to quantify its detection uncertainty by taking into account the effect of random sensor locations on inspection signals. Recent studies of the authors proposed a model that adopts Monte Carlo simulation to numerically obtain the distribution of inspection signals influenced by random sensor locations. This model can evaluate the effect not only of multiple defect dimensions but also of the placement of sensors on the detection uncertainty. However, its effectiveness has only been confirmed using pseudo-experimental signals generated by artificial pollution. This study aims to examine the effectiveness of the proposed model in quantifying the detection uncertainty of SHM methods using the experimental signals of low frequency electromagnetic monitoring for inspecting wall thinning in pipes. The results confirm the capability of the proposed model to correctly characterize the distribution of inspection signals affected by random sensor locations and to determine the reasonable probability of detection.
Keywords: Non-normal noise, probabilistic calibration, non-parametric probability density function, finite element simulation
