Railway wheels and rails do not achieve full wear life expectancy due to the combination of wear, plastic deformation, and surface, subsurface, and deep subsurface cracks. Sixty-seven percent of wheel replacement and maintenance in North America is associated with tread damage [1]. Spalling and shelling are the two major types of wheel tread damage observed in railroad operations. Spalling and slid flat defects occur due to skidded or sliding wheels caused by, in general, unreleased brakes. Tread shelling (surface or shallow subsurface fatigue) occurs due to cyclic normal and traction loads that can generate rolling contact fatigue (RCF). Shelling comprises about half of tread damage related wheel replacement and maintenance. The annual problem size associated with wheel tread RCF is estimated to be in the tens of millions of dollars. The total cost includes maintenance, replacement, train delays and fuel consumption. To study the conditions under which RCF damage accumulates, a 36-ton axle load aluminum body coal car was instrumented with a high accuracy instrumented wheelset (IWS), an unmanned data acquisition (UDAC) system, and a GPS receiver. This railcar was sent to coal service between a coal mine and power plant, and traveled approximately 1,300 miles in the fully loaded condition on each trip. Longitudinal, lateral, and vertical wheel-rail forces were recorded continuously during four loaded trips over the same route using the same railcar and instrumentation. The first two trips were conducted with non-steering 3-piece trucks and the last two trips were conducted with passive steering M-976 compliant trucks to allow comparison of the wheel load environment and RCF accumulation between the truck types. RCF initiation predictions were made using “Shakedown Theory” [2]. Conducting two trips with each set of trucks allowed for analysis of the effects of imbalance speed conditions (cant deficiency or excess cant) at some curves on which the operating speeds varied significantly between trips.
Ensuring rail safety is a priority for the Federal Railroad Administration (FRA) and the railroad industry in North America. One such endeavor is to leverage Wireless Sensor Networks (WSN) to monitor and report in real-time the status of mechanical and electrical components for each railcar, and in conjunction with other railroad subsystems, ensure the safety, security and integrity of transported goods. The envisioned solution utilizes sensors installed on each railcar to form a train-based wireless network and collect real-time (or near real-time) information on different elements of a train and transmit aggregated information to the locomotive, dispatch centers or regional offices for early fault detection and accident prevention. The railroads have been interested in using a standards-based low-cost communication protocol for this purpose, such as IEEE 802.15.4, often referred to as ZigBee. Our results show, however, that ZigBee was designed for smaller wireless networks, such as a single railcar. It exhibits several critical problems associated with the unique network topology found on a freight train and the size of such a network. In essence, the network would take the shape of a very long chain of nodes. Some of the problems stemming from this topology are excessively long synchronization delays for establishing the network along the entire train, severe problems with route discovery and maintenance necessary for selecting the next relay node along the chain, aggregation of data errors and a resulting unacceptable packet loss rate, the lack of a traffic prioritization mechanism to protect important packets such as those containing critical alarms of equipment failure, and many more. In this paper, we describe our findings and experiences in our evaluation of ZigBee for railcar monitoring onboard freight trains, a detailed analysis of the identified problems and their impact on the envisioned railcar monitoring as well as discuss potential solutions to these problems.
Diesel fuel carriage in locomotives, while safe in normal operational conditions, presents a potential hazard in the event of serious accident or derailment. Development of an effective mitigation method against this hazard requires an understanding of operational conditions that lead to fuel spill and fire. This paper describes a study of fire hazard stemming from rail accidents and potential approaches to mitigation. Data for the study was obtained from a large sample of National Transportation Safety Board (NTSB) investigation reports for accidents involving both freight and passenger locomotive accidents over a 10-year period. Approximately 25% of the events reviewed resulted in fuel release. In addition, of the events that resulted in fuel loss, a large majority (almost 70%) resulted in fire. Most cases with major fires led to loss of life and/or property, including destruction of multiple locomotives. Typical road locomotives carry 3,000–4,500 gallons of diesel fuel during normal operation. As the locomotive consumes fuel, large volumes are available for vapor generation within the tank. In a post-collision scenario, the vapor that vents to the atmosphere at temperatures close to flash point of the fuel presents a significant fire hazard. Further, flammable mists can be generated by the sprays that develop due to fuel leaks from the post-impact movement of a train. Previous laboratory tests on a scaled tank demonstrated that fire in a fuel-rich vapor can flash back inside the tank causing an explosion or a large fire. This paper also assesses potential technologies to prevent or mitigate fire hazards in locomotive fuel tanks. These include fuel tank leak prevention or reduction of outflow from breached fuel tanks, monitoring vapor concentration within fuel tanks, and limiting vapor concentrations inside tank to maintain levels below the Lower Explosive Limit (LEL). Potential benefits of the latter method include minimization of pollution from escaping vapor as well as partial recovery of reusable fuel from vapor.
Wheel shelling is the cause of a large portion of high impact wheels. The impact loads produced by shelled wheels can have a damaging effect on track components and rolling stock components such as roller bearings. Shelling is the result of accumulated rolling contact fatigue (RCF) on the wheel tread surface. To investigate the specific conditions in which RCF occurs, wheel load environment data was collected from a car with three-piece trucks running in revenue service. This data was analyzed in order to assess the predicted wheel RCF through the use of shakedown theory. An inspection team was dispatched to several track sites to record relevant information including a visual assessment of rail RCF, rail transverse profile, rail age, and friction conditions. Track inspections were conducted at locations where RCF was predicted and at nearby locations with similar curvature where RCF was not predicted. Conclusions from this work are the following: • The curve unbalance condition, which is a combination of curvature, track superelevation, and train speed, is an important factor in RCF. • Wheel/rail coefficient of friction in curves can be a factor in RCF. • Rail profile and track condition were not found to be major factors in this analysis. • Observed rail RCF condition correlated reasonably well with predictions when considering extenuating factors such as rail age and curve unbalance conditions. • Confidence was increased in previous simulation results involving three-piece trucks due to good correlation with the results of the current work. The simulation results suggest that the use of AAR approved M-976 trucks should reduce RCF. This work was funded by the Federal Railroad Administration (FRA) and the Wheel Defect Prevention Research Consortium (WDPRC), a group that includes railroads, private car owners, and industry suppliers.
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