Publications (3)0 Total impact
Conference Proceeding: A computer vision system for monitoring the energy efficiency of intermodal trains[show abstract] [hide abstract]
ABSTRACT: Intermodal trains are typically the fastest trains operated by North American freight railroads. It is thus ironic that these trains tend to have the poorest aerodynamic characteristics. Because of constraints imposed by equipment design and diversity, there are often large gaps between intermodal loads and these trains incur greater aerodynamic penalties and increased fuel consumption compared to other trains. We conducted train energy analyses of the most common intermodal train configurations operated in North America. It was found that matching intermodal loads with cars of appropriate length reduces the gap length thereby improving airflow. Properly matching cars with loads also avoids use of cars that are longer and thus heavier than necessary. For double stack containers on well cars, train resistance may be reduced by as much as 9% and fuel savings by 0.52 gallon per mile per train. Proper loading of intermodal trains is therefore important to improving energy efficiency. We have developed a wayside machine vision system that automatically scans passing trains and assesses the aerodynamic efficiency of the loading pattern. Machine vision algorithms are used to analyze these images and detect and measure gaps between loads and develop a quantitative index of the loading efficiency of the train. Integration of this metric that we call "slot efficiency" can provide intermodal terminal managers feedback on loading performance for trains and be integrated into the software support systems used for loading assignmentRail Conference, 2006. Proceedings of the 2006 IEEE/ASME Joint; 05/2006
Conference Proceeding: Improving the efficiency and effectiveness of railcar safety appliance inspection using machine vision technology[show abstract] [hide abstract]
ABSTRACT: Before a train departs a yard, many aspects of the freight cars and locomotives undergo inspection, including their safety appliances. Safety appliances are handholds, ladders and other objects that serve as the interface between humans and railcars during transportation. Federal safety rules govern the design and condition of safety appliances. The current car inspection process is primarily visual making it laborious, redundant, and generally lacking of memory. There exists a potential to increase both the effectiveness and efficiency of safety appliance inspections by utilizing machine vision technology to enhance the railcar inspection process. Machine vision consists of capturing digital video and using algorithms capable of detecting and analyzing the particular objects or patterns of interest. Computer algorithms can objectively inspect railcars without tiring or becoming distracted and can also focus on certain parts of the railcar not easily seen by an inspector on the ground. Thus far, algorithms have been developed that can detect deformed ladders, handholds, and brake wheels on open-top gondolas and hoppers. Next, visual learning will be employed to teach the algorithm the differences between Federal Railroad Administration (FRA) safety appliance defects and other types of deformation not requiring a car to be bad ordered. The final product will be a wayside inspection system capable of detecting safety appliance defects on passing railcars.Rail Conference, 2006. Proceedings of the 2006 IEEE/ASME Joint; 05/2006
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ABSTRACT: Intermodal (IM) trains are typically the fastest freight trains operated in North America. The aerodynamic characteristics of many of these trains are often relatively poor resulting in high fuel consumption. However, considerable variation in fuel efficiency is possible depending on how the loads are placed on railcars in the train. Consequently, substantial potential fuel savings are possible if more attention is paid to the loading configuration of trains. A wayside machine vision (MV) system was developed to automatically scan passing IM trains and assess their aerodynamic efficiency. MV algorithms are used to analyse these images, detect and measure gaps between loads. In order to make use of the data, a scoring system was devel-oped based on two attributes – the aerodynamic coefficient and slot efficiency. The aerodynamic coefficient is calculated using the Aerodynamic Subroutine of the train energy model. Slot effi-ciency represents the difference between the actual and ideal loading configuration given the particular set of railcars in the train. This system can provide IM terminal managers feedback on loading performance for trains and be integrated into the software support systems used for loading assignment.