Article Article
A Detection Sensitivity Analysis Model for Structural Health Monitoring to Inspect Wall Thinning considering Random Sensor Location

Structural health monitoring (SHM), which allows the detection of defects at an early stage by attaching sensors to the target, is an effective method of enhancing the reliability and the safety of important engineering structures. One of the practical difficulties of SHM is that usually a large area must be monitored using a limited number of sensors fixed at certain locations. And the sensor placement is a decisive contributor to the detection capability of SHM because measured signals generally depend on the location of a defect with respect to a sensor. In order to quantify the detection sensitivity more reasonably, this study proposes an analytical method based on a closed-form probability density function and a numerical method based on Monte Carlo simulation to quantify the detection sensitivity, taking into account the randomness of sensor location. The effectiveness of the proposed detection sensitivity analysis model has been examined using simulated inspection signals of low frequency electromagnetic monitoring for detecting full circumferential pipe wall thinning.



V. Kain, Proc. Eng. 86, 576–588 (2014). DOI: 10.1016/j.proeng.2014.11.083.

P. Cawley, Struct.HealthMonit. 17 (5), 1225–1244 (2018). DOI: 10.1177/1475921717750047.

Z. Liu and Y. Kleiner, IEEE Sens. J. 12 (6), 1987–1992 (2012). DOI: 10.1109/JSEN.2011.2181161.

C. R. Farrar and N. A. J. Lieven, Philos. Trans. A Math. Phys. Eng. Sci. 365 (1851), 623–632 (2006). DOI: 10.1098/rsta.2006.1927.

J. C. Aldrin, et al., AIP 1211 (1), 1965–1972 (2010).

D. Straub, et al., Value of Information: A Roadmap to Quantifying the Benefit of Structural Health Monitoring (2017).

J. F. C. Markmiller and F. K. Chang, Struct. Health Monit. 9 (1), 25–39 (2010). DOI: 10.1177/1475921709349673.

V. Janapati, et al., Struct. Health Monit.. 15 (2), 143–161 (2016). DOI: 10.1177/1475921715627490.

Y. H. Teo, et al., Theor. Appl. Fract. 52 (1), 40–49 (2009). DOI: 10.1016/j.tafmec.2009.06.007.

M. Azarbayejani, et al., Smart Mater. Struct. 17 (5), 055019 (2008). DOI: 10.1088/0964-1726/17/5/055019.

H. Song, N. Yusa, and H. Hashizume, Mater. Trans. 59 (8), 1348–1353 (2018). DOI: 10.2320/matertrans.M2018057.

A. C. Davison and D. V. Hinkley, Bootstrap Methods and Their Application (Cambridge university press, 1997), pp. 197.

Y. C. Chen, Biostat. Epidemiol. 1 (1), 161–187 (2017). DOI: 10.1080/24709360.2017.1396742.

J. F. Schutte, et al., Int. J. Numer. Methods Eng. 61 (13), 2296–2315 (2004). DOI: 10.1002/nme.1149.


Usage Shares
Total Views
69 Page Views
Total Shares
0 Tweets
0 PDF Downloads
0 Facebook Shares
Total Usage