Commercially available off-the-shelf sensors (COTS) on rail tank cars can help improve the safety of transportation of dangerous goods by rail.&nbsp; The real-time data collected by these sensors can provide regulators, operators, and shippers with better visibility of the tank cars that carry dangerous goods.&nbsp; Further implementation of data analytics algorithms to the real-time data may also identify and provide warnings about potential operational risks related to events such as extremely high impacts and abnormal movement of tank cars.The main objective of this study is to gain a better understanding of the operating environment that tank cars and their commodities experience in revenue service.&nbsp; The work also serves as a pilot study to assess the feasibility and benefit of sensor technologies to tank car fleets to provide better visibility.&nbsp;In the field study, 25 tank cars that had Lat-Lon solar tracking units (STUs) installed were monitored in revenue operation.&nbsp; The STUs collect data about GPS location, speed, impact, hand brake status, etc.&nbsp; A database was developed to collect and house the data.&nbsp; Over a six (6) month period over 140,000 events have been recorded in the database and then processed. Information such as high or multiple impacts, abnormal hand brake position, etc.&nbsp; The analytics is performed with the commercial software Tableau, as well as using internally developed Python and Matlab code.The present pilot study shows that a system can be setup to extract important alerts from these sensors to help provide better visibility of the operation of tank cars used in transporting dangerous goods.&nbsp; The analysis in the present study has focused on impact accelerations, hand brake status, normalized speed, and temperature.&nbsp; Several key events have been identified and the data is presented and discussed. The results demonstrate the usefulness of monitoring rail cars carrying dangerous goods using commercially available over the shelf sensors (COTS).&nbsp; The enormous amount of data collected can be used in more advanced data analytics and machine learning algorithms to provide valuable insights into the movements of dangerous goods.

A Field Demonstration Study of Using Off-the-Shelf Sensors to Monitor Rail Tank Cars Carrying Dangerous Goods

Paper Proceedings: https://asmedigitalcollection.asme.org/JRC/JRC2021/volume/84775

2021 Joint Rail Conference (JRC2021)

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