Interface - The Journal of Wheel/Rail Interaction

WAYSIDE DETECTION SYSTEMS

Wayside Detection Systems Move to the Forefront of the Stress State Landscape (continued)

Political/economic issues notwithstanding, the industry is sold on the use of technology to identify equipment, such as high-impact-load wheels and bad-actor vehicles, and track conditions that generate the highest, most destructive forces and induce the most stress into the system. This includes vehicle/track conditions that taken individually may be within spec, but collectively may generate very high stresses that damage the infrastructure and ultimately lead to derailments—the manifestation of stress in the system.

“We want to identify cars that are behaving badly—those that are generating excessive lateral or vertical loads—before they damage the track, and identify small problems before they turn into big ones,” TTCI's Cummings said.

The TTCI has begun using lateral and vertical wheel force measurements along with axle angle of attack data to identify poor-performing trucks that cause excessive track or vehicle degradation. Performance data are used to produce indices that can identify potential problems in three general categories: Derailment potential; L/V ratios; and misalignment, or tracking errors, in curves. Tracking errors include:

• axle misalignment.

• truck warp, which typically occurs when a worn truck becomes un-square. This tends to cause high lateral forces in curves and excessive truck rotational resistance.

• excessive truck rotational resistance, which can be due to extremely dry centerbowls or improperly set up constant-contact side bearings.

In order to investigate these issues, the TTCI developed tracking error-based indices for truck warp and truck rotation. Researchers identified and inspected the bad actor trucks beyond the 99th percentile at one test site. Of the cars that were inspected, 60% had obvious defects, such as broken springs, worn wedges and damaged side bearings—the types of defects that are billable under AAR interchange rules. Another 20% alarmed on safety limits, such as high L/V ratios. The remaining 20% had no obvious defects. When put back into service, however, that remaining 20% showed repeated poor performance.

Further examination of 100 cars over 18 months indicates that cars with similar components and gross loadings tend to perform similarly. "The 60% best-acting cars always perform well," Cummings said. "The remaining 40% contain a mix of trucks that perform well sometimes and poorly at others." A portion of the worst performing 5% of these cars sometimes performs poorly, but sometimes performs normally. Why is that?

Depending on factors such as the wheel/rail coefficient of friction or the dynamic activity of the vehicle, which is related to speed, lading and truck warp position, trucks may perform differently each time they pass a TPD, Cummings said. A truck that is warped on one pass may be knocked square by a switch or other track feature and be perfectly square when passing the next TPD. Differences in rotational resistance, in which a truck has a tendency to move more easily in one direction than the other, can also play a role. This performance “elasticity,” which is more prominent for the worst-of-worst than for the best-of-best acting trucks, is exacerbated as the coefficient of friction increases.

“While good actors are always good, bad actors are sometimes bad, sometimes good,” Cummings said. This has made it difficult to draw trend lines and make predictions about when trucks are in need of repair. Still, TPDs represent a tool that will help railroads perform the type of cost/benefit analysis needed to establish the economic justification to remove or fix trucks that exceed a predetermined limit.

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