Experimental verification of improved probability of detection model considering the effect of sensor's location on low frequency electromagnetic monitoring signals

Haicheng Song, Noritaka Yusa

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)377-384
Number of pages8
JournalInternational Journal of Applied Electromagnetics and Mechanics
Volume64
Issue number1-4
DOIs
Publication statusPublished - 2020

Keywords

  • Non-normal noise
  • finite element simulation
  • non-parametric probability density function
  • probabilistic calibration

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering
  • Electrical and Electronic Engineering

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