Condition Monitoring of H e avy-Axl e Load Rail c ar Stru
1 Railroad Engineering Program, Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Newmark Civil Engineering Laboratory 205 N. Mathews Ave., Urbana IL, 61801 USA 2 School of EECS, Oregon State University, Kelly Engineering Center, Corvallis OR, 97331 USA 3 Department of E lectrical and Computer Engineering, Computer Vision and Robotics Laboratory, University of Illinois at Urbana-Champaign, Beckman Institute for Advanced Science and Technology, 405 N. Mathews Ave., Urbana IL, 61801 USA
Limits to human optimization in inspection performance
As inspection moves from unaided human skills to human-computer hybrid tasks, there is a need for models of the human and the computer which have common parameters. With appropriate models, functions can be allocated to produce optimal designs, and assistance provided to the human inspector via job aids and training. A model was developed of the human in a two-component compound inspection task consisting of search and decision. Optimizing this model showed that the choice of optimal values of parameters in the two submodels was independent. Ten subjects were tested on a two-component inspection task, using components which had earlier been validated separately. Subjects showed some aspects of optimum behaviour, for example sub-model independence, stopping the search after an integral number of scans, and varying their decision criteria to respond to the probability and cost structure. However, in this more complex task, subjects often reverted to simpler decision rules, for example always stopping the search after one scan or accepting (or rejecting) all potential defects detected. The implication for hybrid automation systems is that humans will need help such as job aids or training if they are to perform optimally when given both search and decision tasks.
Machine Vision Analysis of the Energy Efficiency of Intermodal Trains
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.