Detection and tracking of cracks based on thermoelastic stress analysis

Abstract

Thermoelastic stress analysis using arrays of small, low-cost detectors has the potential to be used in structural health monitoring. However, evaluation of the collected data is challenging using traditional methods, due to the lower resolution of these sensors, and the complex loading conditions experienced. An alternative method has been developed, using image decomposition to generate feature vectors which characterize the uncalibrated map of the magnitude of the thermoelastic effect. Thermal data have been collected using a state-of-the-art photovoltaic effect detector and lower cost, lower thermal resolution microbolometer detectors, during crack propagation induced by both constant amplitude and frequency loading, and by idealized flight cycles. The Euclidean distance calculated between the feature vectors of the initial and current state can be used to indicate the presence of damage. Cracks of the order of 1 mm in length can be detected and tracked, with an increase in the rate of change of the Euclidean distance indicating the onset of critical crack propagation. The differential feature vector method therefore represents a substantial advance in technology for monitoring the initiation and propagation of cracks in structures, both in structural testing and in-service using low-cost sensors.

Comparing full-field data from structural components with complicated geometries

“…Similarly, the decomposition algorithm only used these areas, with locations containing poor data masked. While this damage monitoring approach has been explored in previous studies [ 25 ], the new damage assessment algorithm increases its readiness for application in industry, where components with complicated shapes are common.…”

“…In the previous study, the data were not decomposed, instead an optical flow algorithm was used to determine the location of the crack tip as the fatigue crack grew [ 23 ]. In recent work, a technique has been developed that uses Chebyshev decomposition to quantify changes to the stress on the surface of a specimen as it is fatigued [ 25 ]. It would not be possible to directly apply this technique to the data shown in figure 8 as the stress field around the bolts was obscured by the sealant and thus needed to be removed.…”

“…As the new decomposition algorithm was not limited to rectangular datasets, it was used to decompose all of the data from the bolted specimen, except for the bolt-heads and areas covered by sealant. When performing condition monitoring, it is not necessary to have an exact representation of the original dataset as long as the representation is of a consistent quality [ 25 ]. Therefore, monomials up to an order of 10 were used as this order was previously found to be adequate for condition monitoring when working with Chebyshev polynomials [ 25 ].…”

“…When performing condition monitoring, it is not necessary to have an exact representation of the original dataset as long as the representation is of a consistent quality [ 25 ]. Therefore, monomials up to an order of 10 were used as this order was previously found to be adequate for condition monitoring when working with Chebyshev polynomials [ 25 ]. Each stress field was decomposed using the new decomposition algorithm, with the first stress field measured at the start of the experiment used as the first dataset in the algorithm described in §2.2, thus serving as the reference state against which each subsequent stress field was compared.…”

A thermal emissions-based real-time monitoring system for in situ detection of fatigue cracks

“…Recent work by some of the current authors has demonstrated the potential for the original equipment manufacturer (OEM) microbolometers to be used for monitoring cracks under constant amplitude sinusoidal loading [20]. In this work by Middleton et al [20], IR responses to constant amplitude sinusoidal loading were captured using an OEM microbolometer. The acquired data was post-processed to show the crack propagation detection capability of such low-cost bolometers.…”