Tag Archives: damage

Condition-monitoring using infrared imaging

If you have travelled in Asia then you will probably have experienced having your health monitored by infrared cameras as you disembarked from your flight.  It has been common practice in many Asian countries since long before the COVID-19 pandemic and perhaps will become more usual elsewhere as a means of easily identifying people with symptoms of a fever that raises their body temperature.  Since, research has shown that infrared thermometers are slightly more responsive as well as quicker and easier to use than other types of skin surface thermometers [1].  In my research group, we have been using infrared cameras for many years to monitor the condition of engineering structures by evaluating the distribution of load or stress in them [see ‘Counting photons to measure stress‘ on November 18th, 2015 and  ‘Insidious damage‘ on December 2nd, 2015].  In the DIMES project, we have implemented a low-cost sensor system that integrates infrared and visible images with information about applied loads from point sensors, which allows the identification of initiation and tracking of damage in aircraft structures [2].  I reported in December 2019 [see ‘When seeing nothing is a success‘] that we were installing prototype systems in a test-bench at Empa.  Although the restrictions imposed by the pandemic have halted our tests, we were lucky to obtain data from our sensors during the propagation of damage in the section of wing at Empa before lockdown.  This is a landmark in our project and now we are preparing to install our system in test structures at Airbus once the pandemic restrictions are relaxed sufficiently.  Of course, we will also be able to use our system to monitor the health of the personnel involved in the test (see the top image of one of my research team) as well as the health of the structure being tested – the hardware is the same, it’s just the data processing that is different.

The image is a composite showing images from a visible camera (left) and processed data from infrared camera overlaid on the same visible image (right) from inside a wing box during a test at Empa with a crack extending from left to right with its tip surrounded by the red area in the right image.  Each nut in the image is about 20 mm in diameter and a constant amplitude load at 1.25 Hz was being applied causing a wing tip displacement of 80 mm +/- 15 mm.

The University of Liverpool is the coordinator of the DIMES project and the other partners are Empa, Dantec Dynamics GmbH and Strain Solutions Ltd.

The DIMES project has received funding from the Clean Sky 2 Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 820951.

 

The opinions expressed in this blog post reflect only the author’s view and the Clean Sky 2 Joint Undertaking is not responsible for any use that may be made of the information it contains.

References

[1] Burnham, R.S., McKinley, R.S. and Vincent, D.D., 2006. Three types of skin-surface thermometers: a comparison of reliability, validity, and responsiveness. American journal of physical medicine & rehabilitation, 85(7), pp.553-558.

[2] Middleton, C.A., Gaio, A., Greene, R.J. and Patterson, E.A., 2019. Towards automated tracking of initiation and propagation of cracks in aluminium alloy coupons using thermoelastic stress analysis. Journal of Nondestructive Evaluation, 38(1), p.18.

Less uncertain predictions

Ultrasound time-of-flight C-scan of the delaminations formed by a 12J impact on a crossply laminate (top) and the corresponding surface strain field (bottom).

Here is a challenge for you: overall this blog has a readability index of 8.6 using the Flesch Kincaid Grades, which means it should be easily understood by 14-15 year olds.  However, my editor didn’t understand the first draft of the post below and so I have revised it; but it still scores 15 using Flesch Kincaid!  So, it might require the formation of some larger scale neuronal assemblies in your brain [see my post entitled ‘Digital Hive Mind‘ on November 30th, 2016].

I wrote a couple of weeks ago about guessing the weight of a reader.  I used some national statistics and suggested how they could be updated using real data about readers’ weights with the help of Bayesian statistics [see my post entitled ‘Uncertainty about Bayesian statistics’ on July 5th, 2017].  It was an attempt to shed light on the topic of Bayesian statistics, which tends to be obscure or unknown.  I was stimulated by our own research using Bayesian statistics to predict the likelihood of failure in damaged components manufactured using composite material, such as carbon-fibre laminates used in the aerospace industry.  We are interested in the maximum load that can be carried by a carbon-fibre laminate after it has sustained some impact damage, such as might occur to an aircraft wing-skin that is hit by debris from the runway during take-off, which was the cause of the Concorde crash in Paris on July 25th, 2000.  The maximum safe load of the carbon-fibre laminate varies with the energy of the impact, as well as with the discrepancies introduced during its manufacture.  These multiple variables make our analysis more involved than I described for readers’ weights.  However, we have shown that the remaining strength of a damage laminate can be more reliably predicted from measurements of the change in the strain pattern around the damage than from direct measurements of the damage for instance, using ultrasound.

This might seem to be a counter-intuitive result.  However, it occurs because the failure of the laminate is driven by the energy available to create new surfaces as it fractures [see my blog on Griffith fracture on April 26th, 2017], and the strain pattern provides more information about the energy distribution than does the extent of the existing damage.  Why is this important – well, it offers a potentially more reliable approach to inspecting aircraft that could reduce operating costs and increase safety.

If you have stayed with me to the end, then well done!  If you want to read more, then see: Christian WJR, Patterson EA & DiazDelaO FA, Robust empirical predictions of residual performance of damaged composites with quantified uncertainties, J. Nondestruct. Eval. 36:36, 2017 (doi: 10.1007/s10921-017-0416-6).