Structural health inspection techniques for railway application - Chua, Aik Tuck (Nanyang Technological University, 2020)
This FYP discusses structural health monitoring (SHM) with regard to railway tracks. Railway infrastructure is extremely costly to maintain. Having accurate and cost effective SHM techniques will reduce maintenance cost as faults that are detected at an earlier stage are often cheaper and easier to rectify. This FYP report proposes two structural health monitoring (SHM) techniques for railway application. The first of which involves Arduino laser sensors. The sensors measure distances between an object and themselves and are used to detect changes in geometry on the track surface up to an accuracy of 1mm. The second SHM technique utilises photogrammetry. It is the focus of this report and is coined Mesh Superimposition Inspection (MSI). In MSI, photographs of real-life objects are taken and used to create accurate three-dimensional (3D) models of the objects, called meshes. Two meshes will be created from the same track section one when the section is new, and the other after several months of wear. The two meshes are then superimposed and inspected for any deviation in geometry. The deviations found are measured and checked against a stipulated threshold to decide if any maintenance is necessary. MSI can detect deviations as small as 0.5mm. MSI relies on four programs Meshroom, Meshlab, Netfabb and GOM Inspect. Meshroom creates 3D models of real-life objects. Meshlab cleans and processes the model. Netfabb scales the model to match real-life dimensions and finally, GOM Inspect superimposes two meshes together to inspect for deviations between them. The four software are studied and tested in this report, and a final workflow is presented. Additionally, this report provides a literature review which encompasses some causes of railway breakdown and their inspection techniques in Singapore, photogrammetry and its applications, and a study of railway wear. The literature review provided valuable insights which guided the formulation of the two SHM methods proposed. Lastly, future developments for both methods are evaluated, including their improvements for commercial application.