| Abstract | Utilizing inspection technologies has demonstrated efficacy in monitoring railway tracks and enhancing the overall safety of railway operations. The introduction of automated track geometry measurement systems and their consequential impact on reducing geometry-related derailments underscores the significance of technological adaptation in the railway sector. Maintaining safe track conditions under future climate projections requires more frequent inspections, and consequently track down time. However, the growing demand for rail transportation and supply chain constraints require railway operators to increase the number of trains along their network, consequently limiting the time available for comprehensive track inspection. In response to this multifaceted challenge, railways are actively exploring emerging technologies and embracing automation to facilitate more frequent inspections, thereby enhancing operational efficiencies while upholding safety standards. Addressing this imperative, the National Research Council of Canada (NRC) has undertaken the development of a prototype instrumented hi-rail truck designed to automate select track inspection activities currently reliant on human visual inspections. Equipped with an array of sensors featuring diverse sensing modalities and a range of perspectives, this instrumented truck generates digital representations of the track and its surrounding environment. The resulting digital models, in conjunction with artificial intelligence algorithms, enable the spatial and temporal identification of certain track issues. This short paper provides an overview of the NRC’s instrumented hi-rail truck, including both the software and hardware components, the opportunities it presents to supplement/replace specific aspects of visual inspections, and some results from its deployment under actual field conditions. |
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