A quantitative thermal analysis of cyclists’ thermo-active base layers

It is well known that clothes used in sporting activity are a barrier for heat exchange between the environment and athlete, which should help in thermoregulation improvement. However, it is difficult to evaluate which top is best for each athlete according to the characteristics of the sport. Researchers have tried to measure the athlete’s temperature distribution during exercise at the base layers of tops with different approaches. The aim of this case study was to investigate the use of thermography for thermo-active base layer evaluation. Six new base layers were measured on one cyclist volunteer during a progressive training on a cycloergometer. As a control condition, the skin temperature of the same volunteer was registered without any layer with the same training. A training protocol was selected approximate to cycling race, which started from the warm-up stage, next the progressive effort until the race finished and at the end ‘‘cool-down’’ stage was over. In order to show which layer provided the strongest and weakest barrier for heat exchange in comparison with environment, the temperature parameters were taken into consideration. The most important parameter in the studies was the temperature difference between the body and the layers, which was changing during the test time. The studies showed a correlation between the ergometer power parameter and the body temperature changes, which has a strong and significant value. Moreover, the mass of every layer was checked before and after the training to evaluate the mass of the sweat exuded during the test. From this data, the layer mass difference parameter was calculated and taken into consideration as a parameter, which may correspond with the mean heart rate value from each training. A high and positive correlation coefficient was obtained between the average heart rate and the mass difference for the base layers. Thermal analysis seems to have a new potential application in the objective assessment of sports clothing and may help in choosing the proper clothes, which could support heat transfer during exercising and protect the body from overheating

Achieving Optimised Infrared Thermography in Innovative Asset Management

Optimised Asset Management in recent years has embraced a rapid diffusion of innovation and disruptive technology, especially with reference to infrared thermography. The integration of this technology and associated communication technologies are improving operational aspects of industry, including the prediction of machine and component failures and as a diagnostic tool in medicine. Most machines in the future will be connected to the Internet of Things (IoT) which will be the gateway to communicating with intelligent assets with self-diagnosing capabilities and expert systems. Asset connectivity and predictive analytics will discern patterns and algorithms leading optimised plant production, and enhanced energy efficiency particularly with reference to machines. Machine learning models will indicate future operation on a real-time basis using big data libraries, tabular databases with particular reference to condition monitoring. This paper concentrates on the application of qualitative and quantitative portable infrared thermography. Successful implementation of an intelligent portable infrared thermography system requires an understanding of the industrial process; the machine operation, its surroundings, and the dynamics of infrared radiation. Optimising and integrating infrared into an asset management subsystem requires correct monitoring equipment selection and accurate data collection including; Optimum radiometer wavelength, background characterization, spatial and thermal resolution and emissivity. This paper concludes with an industrial atlas of infrared normal and abnormal images, which are a useful reference in determining the various conditions in medical diagnostics with the primary intention to identify early onset of problems as part of an optimised asset management system