FBG Temperature Sensor Data Processing System Based on LabVIEW

Abstract

Train and high-speed rail tracks are susceptible to different damage modes such as cracks, corrosion, and degradation. If damage is left undetected, these initial damage sites can propagate and cause track failure and derailment, similar to the 2016 Amtrak train derailment incident in Kansas. However, damage sites in these tracks can be hard to detect, particularly because they start small and can be hidden from plain sight (e.g., due to dirt, debris, and corrosion that would otherwise obstruct its view). Therefore, the long-term goal is to develop a portable damage detection tool that can scan the cross-section of a track and be able to identify the location and severity of crack damage. As a step towards this goal, this study is focused on using numerical simulations for assessing the validity of using non-contact tomography for track damage monitoring and localization. Specifically, the technique is based on electrical capacitance tomography. A set of non-contact electrodes arranged in a circular ring pattern can be selectively excited using an alternating current signal. By measuring the electrical response at all other electrodes, one can use this dataset for solving an inverse problem to reconstruct a map of the electrical properties of the sensing region (i.e., the area enclosed by the ring of electrodes). Like computed tomography images, the results show an electrical permittivity map of the cross-section. Any localized changes in the permittivity map reveals locations in which there might be damage in the track and is worthy of more detailed assessment. A set of numerical simulation case studies has been done to assess the potential of using this technique for track monitoring. Preliminary results obtained show that this technique was able to detect, quantify, and locate the change in permittivity distribution in the modeled rail track, thereby suggesting its potential as a noninvasive spatial damage detection tool.