Non-Contact Smartphone-Based Monitoring of Thermally Stressed Structures

Abstract

The in-situ measurement of thermal stress in beams or continuous welded rails may prevent structural anomalies such as buckling. This study proposed a non-contact monitoring/inspection approach based on the use of a smartphone and a computer vision algorithm to estimate the vibrating characteristics of beams subjected to thermal stress. It is hypothesized that the vibration of a beam can be captured using a smartphone operating at frame rates higher than conventional 30 Hz, and the first few natural frequencies of the beam can be extracted using a computer vision algorithm. In this study, the first mode of vibration was considered and compared to the information obtained with a conventional accelerometer attached to the two structures investigated, namely a thin beam and a thick beam. The results show excellent agreement between the conventional contact method and the non-contact sensing approach proposed here. In the future, these findings may be used to develop a monitoring/inspection smartphone application to assess the axial stress of slender structures, to predict the neutral temperature of continuous welded rails, or to prevent thermal buckling.

Keywords: computer vision; neutral temperature; nondestructive testing; smartphone technology; structural health monitoring; thermal stress.

Figure 1

(a) Synthetic target object in gray scale; (b) synthetic target object displaced 0.1 px to the right; and (c,d) thresholded version at luminance level 128 of objects in (a,b), respectively. Notice the differences in the four corners of the rectangle.

Figure 2

Multilevel threshold of a grayscale object. Each of the thresholded versions will be treated as a separated sequence and its movement will be analyzed following the algorithm here explained.

Figure 3

Fourier transform of the signal obtained from 8 equidistant thresholded levels calculated for the represented ROI applied on a grayscale sequence of a vibrating fork (Figure taken from [36]).

Figure 4

Photos of the experimental setup: (a) yhin beam and smartphone used to record the videos; (b) infrared camera pointing at the thin beam specimen; (c) close-up view of the thin beam and the accelerometer used to measure the vibrations; and (d) side view of the thick beam.

Figure 5

Thermal images of the thin (a,b) and thick (c,d) beams at the initial and the highest temperature in the third heating ramp. The rectangular frame emphasizes the area of the beam considered to compute the average temperature and includes the entire free length (see Table 1) of the specimens.

Figure 6

Slender beam testing: (a) Axial stress measured with the MTS machine as a function of the average beam temperature measured through the infrared camera; and (b) theoretical axial stress as a function of the exact uniform temperature in the thin beam.

Figure 7

Thin beam testing: Frame example and close-up view of the ROI.

Figure 8

Accelerometer readings and corresponding fast Fourier Transforms associated with: (a,b) the initial temperature; and (c,d) the highest temperature of the third heating ramp.

Figure 9

Frequency of vibration of the thin beam as measured with the multi-thresholded technique at: (a) the initial temperature; and (b) the highest temperature of the third heating ramp.

Figure 10

Slender beam testing. Natural frequency as a function of the axial stress during the: (a) first; (b) second; and (c) third thermal cycles. The letter H indicates the heating ramp, while the letter C indicates the cooling ramp.

Figure 11

Slender beam testing: (a) Natural frequency estimated with the multi-thresholded image processing technique as a function of the axial stress measured with the MTS. All the empirical data lay on a line function. (b) Natural frequency as a function of the axial stress as predicted by Equation (6).

Figure 12

Slender beam testing: (a) Estimated stress with the smartphone compared to the stress measured with the MTS; and (b) estimated stress with the accelerometer data compared to the stress measured with the MTS.

Figure 13

Thick beam testing: Axial stress as a function of the average beam temperature. The solid squares indicate the heating ramp, while the solid circles represent the cooling ramp.

Figure 14

Thick beam testing: Estimated stress from the accelerometer and the smartphone as a function of the stress measured from the MTS machine. The letter H indicates the heating ramp, while the letter C indicates the cooling ramp.

Figure 15

Thick beam testing: (a) Estimated stress with the smartphone compared to the stress measured with the MTS machine; and (b) estimated stress with the accelerometer data compared to the stress measured with the MTS.