Exploring Wireless Strain Measurement for Civil Infrastructure

Decker, Brandon Richard; Rasheed, Hayder A.; Peterman, Robert J.; Esmaeily, Asad; Melhem, Hani

doi:10.1111/j.1475-1305.2008.00518.x

Cited by 1 publication

(1 citation statement)

References 2 publications

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“…Strain gauges are often employed in structural health monitoring (SHM) networks installed with the purpose of monitoring the dynamic behaviour of bridges and other Civil Engineering works of art. () In such critical systems, sensor faults and structural damage need to be assuredly discriminated. () Thus, the performance of those systems depends critically on an efficient sensor fault detection process.…”

Section: Introductionmentioning

confidence: 99%

Double bridge circuit for self‐validated structural health monitoring strain measurements

Reis

Costa

2018

Strain

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In structural health monitoring (SHM) applications, sensor faults and structural damage need to be assuredly discriminated. A self‐diagnosis strain sensor operating in a continuous online SHM scenario is considered. The strain sensor is based on full electric resistance strain gauge Wheatstone bridges. The state of the art shows that such a sensor has not yet been developed. The loop current step response (LCSR) is a well‐known method to detect strain gauge debonding. However, applying the LCSR method to a full strain gauge Wheatstone bridge has some limitations analysed in this paper. To enable the use of the LCSR method in an online SHM scenario, the double bridge circuit is proposed in this work. Two new strain gauge debonding fault detection methods and a new debonding fault isolation method—based on the double bridge circuit measurements—are proposed and evaluated. Two new sensor fusion weighting approaches are also proposed and evaluated—to achieve strain gauge debonding fault tolerance on the double bridge circuit. The experimental results show that the proposed methods can detect, isolate, and tolerate a strain gauge grid debonding fault and can be applied in an online SHM self‐diagnosis sensor scenario.

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Section: Introductionmentioning

confidence: 99%