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Besides detecting those changes, another usage of this dataset is for developing or validating data fusion methods. As a validation of our dataset, we have been able to detect changes in the tracks, which correspond to known maintenance activities. This could be a benchmark dataset for comparing different vibration-based damage diagnosis algorithms. The data were recorded from two light rail vehicles for four years with a variety of influential factors. We have been continuously collecting data on the trains’ position using GPS and their dynamic responses using accelerometers in addition, our dataset includes environmental data as the trains were running on the track and the track maintenance logs from the light-rail operator.Īlthough there are some acceleration datasets for structure vibration testing 12, human activity recognition 13, senior fall detection 14 and gait recognition 15, at the time of writing, the DR-Train dataset is the only one to include multi-channel and high-frequency acceleration signals and GPS positions of light rail vehicles. We instrumented one train in Fall 2013, and a second train in Summer 2015. Over time, we learned how the trains respond to each section of track and use a data-driven approach to detect changes to the track condition relative to its historical baseline. We monitored Pittsburgh’s light rail network from sensors placed on passenger trains, as a more economical monitoring approach than either visual inspection or inspection with dedicated track vehicles. Also, sensors installed on in-service trains can provide continuous monitoring of the track without affecting regular operations.
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In recent years, researchers have proposed many indirect track inspection methods using sensors, such as accelerometers and GPS, installed on in-service trains for track geometry monitoring and change detection 3, 4, 5, 6, 7, 8, 9, 10, 11 since it can be more reliable than visual inspection and costs less than inspection using a track-geometry car. Due to its high cost and interruptions, it is difficult to conduct frequent inspections using a track-geometry car. While inspection using a dedicated track geometry car can provide accurate track geometry data, it requires interruptions of regular train operations, and each inspection session has a more expensive cost than visual inspection. Visual inspection is neither reliable nor convenient. In practice, two traditional approaches are usually adopted to inspect track infrastructure: (1) visual inspection and (2) inspection using a dedicated track geometry car. To ensure safety and reduce maintenance cost, it is necessary to develop low-cost and reliable techniques to monitor the status of railroad networks continuously, especially track geometries. However, in 2017, the Federal Railroad Administration still reported 11,699 train accidents/incidents including 1,223 derailments and 470 track-caused accidents/incidents in the nation 2.
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makes $9.7 billion capital investment in maintaining the network, which is comprised of almost 140,000 miles of track and over 100,000 bridges in 2015 1. The private freight rail industry in the U.S. The data, which is stored in a MAT-file format, can be conveniently loaded for various potential uses, such as validating anomaly detection and data fusion as well as investigating environmental influences on train responses. The dataset also includes corresponding GPS positions of the trains, environmental conditions (including temperature, wind, weather, and precipitation), and track maintenance logs. Such an approach will result in more reliable and economical ways to monitor rail infrastructure. This dataset provides dynamic responses of in-service trains via vibration data collected by accelerometers, which enables a low-cost way of monitoring rail tracks more frequently. Specifically, the dataset contains measurements from multiple sensor channels mounted on two in-service light rail vehicles that run on a 42.2-km light rail network in the city of Pittsburgh, Pennsylvania. We present DR-Train, the first long-term open-access dataset recording dynamic responses from in-service light rail vehicles.