Continuous Welded Rail (CWR) practice is used in modern railroads to alleviate maintenance issues associated with joints and to improve ride quality. The absence of expansion joints, however, leads to long rail segments that are prone to the development of longitudinal thermal stresses that may cause track buckling, or rail pull-apart. A critical parameter in the susceptibility of the track to failure due to thermal loading is the Rail Neutral Temperature. This parameter is the temperature at which the rail is stress free. Rail stress management practices depend on the knowledge of the total net stress in the rail and the RNT. Current in-situ rail stress measurement techniques are destructive and disruptive of service. A new non contacting, nondestructive methodology is under development at the University of South Carolina for RNT and longitudinal stress measurements. The method is based on stereo vision image acquisition and Digital Image Correlation (DIC) for acquiring the full field shape, deformation and strain measurements taken during a thermal cycle. The thermal cycle can be natural or induced. This paper discusses the effects of the way the rail is heated on the RNT and stress measurements.

Wayside hot-box detectors (HBDs) are devices that are currently used to monitor bearing, axle, and brake temperatures as a way of assessing railcar component health and to indicate any possible overheating or abnormal operating conditions. Conventional hot-box detectors are set to alarm whenever a bearing is operating at a temperature that is 94.4°C (170°F) above ambient, or when there is a 52.8°C (95°F) temperature difference between two bearings that share an axle. These detectors are placed adjacent to the railway and utilize an infrared sensor in order to obtain temperature measurements. Bearings that trigger HBDs or display temperature trending behavior are removed from service for disassembly and inspection. Upon teardown, bearings that do not exhibit any discernible defects are labeled as “non-verified”. The latter may be due to the many factors that can affect the measurement of HBDs such as location of the infrared sensor and the class of the bearing among other environmental factors. A field test was performed along a route that is more than 483 km (300 mi) of track containing 21 wayside hot-box detectors. Two freight cars, one fully-loaded and one empty, and one instrumentation car pulled by a locomotive were used in this field test. A total of 16 bearings (14 Class F and 2 Class K) were instrumented with K-type bayonet thermocouples to provide continuous temperature measurement. The data collected from this field test were used to perform a systematic study in which the HBD IR sensor data were compared directly to the onboard thermocouple data. The analyses determined that, in general, HBDs tend to overestimate Class K bearing temperatures more frequently than Class F bearing temperatures. Additionally, the temperatures of some bearings were underestimated by as much as 47°C (85°F). Furthermore, the HBD data exhibited some false trending events that were not seen in temperature histories recorded by the bayonet thermocouples. The findings from the field test suggest that HBDs may inaccurately report bearing temperatures, which may contribute to the increased percentage of non-verified bearing removals. To further investigate the accuracy of the wayside detection systems, a dynamic test rig was designed and fabricated by the University Transportation Center for Railway Safety (UTCRS) research team at the University of Texas Rio Grande Valley (UTRGV). A mobile infrared sensor was developed and installed on the dynamic tester in order to mimic the measurement behavior of a HBD. The infrared temperature measurements were compared to contact thermocouple and bayonet temperature measurements taken on the bearing cup surface. The laboratory-acquired data were compared to actual field test data, and the analysis reveals that the trends are in close agreement. The large majority of temperature measurements taken using the IR sensor have been underestimated with a similar distribution to that of the data collected by the HBDs in field service.

The railroad industry utilizes wayside detection systems to monitor the temperature of freight railcar bearings in service. The wayside hot-box detector (HBD) is a device that sits on the side of the tracks and uses a non-contact infrared sensor to determine the temperature of the train bearings as they roll over the detector. Various factors can affect the temperature measurements of these wayside detection systems. The class of the railroad bearing and its position on the axle relative to the position of the wayside detector can affect the temperature measurement. That is, the location on the bearing cup where the wayside infrared sensor reads the temperature varies depending on the bearing class (e.g., class K, F, G, E). Furthermore, environmental factors can also affect these temperature readings. The abovementioned factors can lead to measured temperatures that are significantly different than the actual operating temperatures of the bearings. In some cases, temperature readings collected by wayside detection systems did not indicate potential problems with some bearings, which led to costly derailments. Attempts by certain railroads to optimize the use of the temperature data acquired by these wayside detection systems has led to removal of bearings that were not problematic (about 40% of bearings removed were non-verified), resulting in costly delays and inefficiencies. To this end, the study presented here aims to investigate the efficacy of the wayside detection systems in measuring the railroad bearing operating temperature in order to optimize the use of these detection systems. A specialized single bearing dynamic test rig with a configuration that closely simulates the operating conditions of railroad bearings in service was designed and built by the University Transportation Center for Railway Safety (UTCRS) research team at the University of Texas Rio Grande Valley (UTRGV) for the purpose of this study. The test rig is equipped with a system that closely mimics the wayside detection system functionality and compares the infrared sensor temperature reading to contact thermocouple and bayonet temperature sensors fixed to the outside surface of the bearing cup. This direct comparison of the temperature data will provide a better understanding of the correlation between these temperatures under various loading levels, operating speeds, and bearing conditions (i.e. healthy versus defective), which will allow for an optimization of the wayside detectors. The impact on railway safety will be realized through optimized usage of current wayside detection systems and fewer nonverified bearings removed from service, which translates into fewer costly train stoppages and delays.

This study is aimed to develop a real-time safety monitoring of kilometer-long joint rails using a distributed fiber optic sensor. The sensor measures the distribution of Brillouin frequency shift along its length with pulse pre-pump Brillouin optical time domain analysis (PPP-BOTDA). The measurement distance and spatial resolution can be up to 25 km and 2 cm, respectively. The fiber optic sensor was first characterized and calibrated for distributed strain and temperature measurement, and then instrumented on a small-scale joint rail-like specimen in laboratory. The specimen was loaded at room temperature, and its strain distribution along the sensor was measured using a Neubrescope with high accuracy and spatial resolution. Given a gage length, the joint open change was determined and visibly identified from the measured strain distribution. Finally, an implementation plan of distributed sensors on a railway is introduced, including sensor deployment, sensor repair when broken, and cost analysis. The gage length at a crack is an important parameter in sensor deployment and investigated using finite element analysis. The results indicate that the distributed sensor can be used successfully to monitor the strain and temperature distributions in joint rails.

The railroad industry uses slow orders, sometimes referred to as speed restrictions, in areas where an elevated rail temperature is expected in order to minimize the risk and consequence of derailment caused by track buckling due to excessive rail temperature. Traditionally, rail temperature has been approximated by adding a constant offset, most often 30°F, to a peak ambient air temperature. When this approximated maximum rail temperature exceeds a given risk threshold, slow orders are usually issued for a predefined period of the day. This “one size fits all” approach, however, is not effective and suitable in all situations. On very warm days, the difference between rail temperature and ambient air temperature can exceed railroad-employed offsets and remain elevated for extended periods of time. A given temperature offset may be well suited for certain regions and track buckling risk-related rail temperature thresholds but less accurate for others. Almost 160,000 hours of rail temperature measurements collected in 2012 across the eastern United States by two Class I railroads and predicted ambient air temperatures based on the National Weather Service’s National Centers for Environmental Prediction (NCEP) data were analyzed using detection theory in order to establish optimal values of offsets between air and rail temperatures as well as times when slow orders should be in place based on geographical location and the track buckling risk rail temperature threshold. This paper presents the results of the analysis and describes an improved procedure to manage heat-related slow orders based on ambient air temperatures.

This paper presents a study of the fatigue life (i.e. number of stress cycles before failure) of Class K cast iron conventional and modified railroad bearing adapters for onboard monitoring applications under different operational conditions based on experimentally validated Finite Element Analysis (FEA) stress results. Currently, freight railcars rely heavily on wayside hot-box detectors (HBDs) at strategic intervals to record bearing cup temperatures as the train passes at specified velocities. Hence, most temperature measurements are limited to certain physical railroad locations. This limitation gave way for an optimized sensor that could potentially deliver significant insight on continuous bearing temperature conditions. Bearing adapter modifications (i.e. cut-outs) were required to house the developed temperature sensor which will be used for onboard monitoring applications. Therefore, it is necessary to determine the reliability of the modified railroad bearing adapter. Previous work done at the University Transportation Center for Railway Safety (UTCRS) led to the development of finite element model with experimentally validated boundary conditions which was utilized to obtain stress distribution maps of conventional and modified railroad bearing adapters under different service conditions. These maps were useful for identifying areas of interest for an eventual inspection of railroad bearing adapters in the field. Upon further examination of the previously acquired results, it was determined that one possible mode of adapter failure would be by fatigue due to the cyclic loading and the range of stresses in the railroad bearing adapters. In this study, the authors experimentally validate the FEA stress results and investigate the fatigue life of the adapters under different extreme case scenarios for the bearing adapters including the effect of a railroad flat wheel. In this case, the flat wheel translates into a periodic impact load on the bearing adapter. The Stress-Life approach is used to calculate the life of the railroad bearing adapters made out of cast iron and subjected to cyclic loading. From the known material properties of the adapter (cast iron), the operational life is estimated with a mathematical relationship. The Goodman correction factor is used in these life prediction calculations in order to take into account the mean stresses experienced by these adapters. The work shows that the adapters have infinite life in all studied cases.

Consistent process control of wheel hardness and residual stresses developed during heat treatment are particularly important considerations for service life and safety of railway wheels. This paper details the process controls strategically located throughout an integrated, fully automated heat treatment system that can heat treat up to 65 railway wheels per hour. New, innovative technology such as in-line temperature measurements that control key process steps, uniform wheel heating and cooling, and quench water temperature and pressure control have resulted in wheel hardness and residual stress values with less statistical variation than older, traditional heat treat methods. Automatic serial number tracking and temperature measurement allow for statistical analysis of heat treat processes. Two years after the commissioning of this $18M facility, the quality and productivity benefits realized are discussed.