Optimizing Power Consumption of Freight Railroad Bearings Using Laboratory Experimental Data

Based on projected freight truck fuel efficiency, freight railroad and equipment suppliers need to identify, evaluate and implement technologies and/or operating practices to maintain traditional railroad economic competitiveness. The railway industry uses systems that record the total energy efficiency of a train but not energy efficiency or consumption by components. Lowering the energy consumption of certain train components will result in an increase in its overall energy efficiency, which will yield cost benefits for all the stakeholders. One component of interest is the railroad bearing whose power consumption varies depending on several factors that include railcar load, train speed, condition of bearing whether it is healthy or defective, and type of defect. Being able to quantify the bearing power consumption, as a function of the variables mentioned earlier, would make it possible to obtain optimal operating condition ranges that minimize energy consumption and maximize train energy efficiency. Several theoretical studies were performed to estimate the power consumption within railroad bearings, but those studies lacked experimental validation. For almost a decade now, the University Transportation Center for Railway Safety (UTCRS) at the University of Texas Rio Grande Valley (UTRGV) has been collecting power consumption data for railroad bearings under various loads, speeds, ambient temperatures, and bearing condition. The objective of this ongoing study is to use the experimentally acquired power consumption to come up with a correlation that can be used to quantify the bearing power consumption as a function of load, speed, ambient temperature, and bearing condition. Once obtained, the model can then be used to determine optimal operating practices that maximize the railroad bearing energy efficiency. In addition, the developed model will provide insight into possible areas of improvement for the next generation of energy efficient railroad bearings. This paper will discuss ongoing work including experimental setup and findings of energy consumption of bearings as function of railcar load, train speed, condition of bearing whether it is healthy or defective, and type of defect. Findings of energy consumption are converted into approximations of diesel gallons to quantify the effect of nominal energy consumption of the bearings and show economic value and environmental impact.

Information System for Railway Inspection using Drone and Image Processing

Train is the main mode of transportation that we often use, the train itself can be used as a tool for shipping goods and mobilizing passengers, this transportation is very unique and has its own path in the form of steel strings across from hundreds of kilometers, railroad bearing structures currently exist which uses concrete and wood, the railroad is very vital and is an important supporting facility. The process of railroad monitoring is complex and complicated, takes a long time, the previous method is simple and conventional by tracing the railroad tracks manually or using a geometric gauge mounted direl or also known as railpod, railpod will follow the rails and will provide report if there is a train track that is damaged, broken or shifted, this research will create an image-based monitoring system using drones as a track monitor, another way is to take pictures using satellite data that will provide clear information about road conditions before being passed by the train, the railroad data processing system by using image processing can display visual responses up to cm in size, the response appears if there is a shift in the path then the system directly provides data in the form of location and shifting paths on the main computer, this system is more c eTat in checking and analyzing train track data with high accuracy and precision up to 90%, in addition to imagery from satellite images can use drones, drones themselves are very easy in maintenance and use and are able to cut production costs and even workplace accidents in the field workers themselves can be avoided because the drone is able to reach the track and railroad that is difficult for example through the tunnel or the railroad track along the hills and densely populated.

Defect Prognostics Models for Spall Growth in Railroad Bearing Rolling Elements

Prevention of railroad bearing failures, which may lead to catastrophic derailments, is a central safety concern. Early detection of railway component defects, specifically bearing spalls, will improve overall system reliability by allowing proactive maintenance cycles rather than costly reactive replacement of failing components. A bearing health monitoring system will provide timely detection of flaws. However, absent a well verified model for defect propagation, detection can only be used to trigger an immediate component replacement. The development of such a model requires that the spall growth process be mapped out by accumulating associated signals generated by various size spalls. The addition of this information to an integrated health monitoring system will minimize operation disruption and maintain maximum accident prevention standards enabling timely and economical replacements of failing components. An earlier study done by the authors focused on bearing outer ring (cup) raceway defects. The developed model predicts that any cup raceway surface defect (i.e. spall) once reaching a critical size (spall area) will grow according to a linear correlation with mileage. The work presented here investigates spall growth within the inner rings (cones) of railroad bearings as a function of mileage. The data for this study were acquired from defective bearings that were run under various load and speed conditions utilizing specialized railroad bearing dynamic test rigs owned by the University Transportation Center for Railway Safety (UTCRS) at the University of Texas Rio Grande Valley (UTRGV). The experimental process is based on a testing cycle that allows continuous growth of railroad bearing defects until one of two conditions are met; either the defect is allowed to grow to a size that does not jeopardize the safe operation of the test rig, or the change in area of the spall is less than 10% of its previous size prior to the start of testing. The initial spall size is randomly distributed as it depends on the originating defect depth, size, and location on the rolling raceway. Periodic removal and disassembly of the railroad bearings was carried out for inspection and defect size measurement along with detailed documentation. Spalls were measured using optical techniques coupled with digital image analysis, as well as, with a manual coordinate measuring instrument with the resulting field of points manipulated in MatLab™. Castings were made of spalls using low-melting, zero-shrinkage bismuth-based alloys, so that a permanent record of the spall geometry and its growth history can be retained. The main result of this study is a preliminary model for spall growth, which can be coupled with bearing condition monitoring tools that will allow economical and effective scheduling of proactive maintenance cycles that aim to mitigate derailments, and reduce unnecessary train stoppages and associated costly delays on busy railways.

Impact of Hysteresis Heating of Railroad Bearing Thermoplastic Elastomer Suspension Pad on Railroad Bearing Thermal Management

It is a known fact that polymers and all other materials develop hysteresis heating due to the viscoelastic response or internal friction. The hysteresis or phase lag occurs when cyclic loading is applied leading to the dissipation of mechanical energy. The hysteresis heating is induced by the internal heat generation of the material, which occurs at the molecular level as it is being disturbed cyclically. Understanding the hysteresis heating of the railroad bearing elastomer suspension element during operation is essential to predict its dynamic response and structural integrity, as well as to predict the thermal behavior of the railroad bearing assembly. The main purpose of this ongoing study is to investigate the effect of the internal heat generation in the thermoplastic elastomer suspension element on the thermal behavior of the railroad bearing assembly. This paper presents an experimentally validated finite element thermal model that can be used to obtain temperature distribution maps of complete bearing assemblies in service conditions. The commercial software package ALGOR 20.3™ is used to conduct the thermal finite element analysis. Different internal heating scenarios are simulated with the purpose of determining the bearing suspension element and bearing assembly temperature distributions during normal and abnormal operation conditions. Preliminary results show that a combination of the ambient temperature, bearing temperature, and frequency of loading can produce elastomer pad temperature increases above ambient of up to 125°C when no thermal runway is present. The higher temperature increase occurs at higher loading frequencies such as 50 Hz, thus, allowing the internal heat generation to significantly impact the temperature distribution of the suspension pad. This paper provides several thermal maps depicting normal and abnormal operation conditions and discusses the overall thermal management of the railroad bearing assembly.

Hysteresis Heating of Railroad Bearing Thermoplastic Elastomer Suspension Element

Thermoplastic elastomers (TPE’s) are increasingly being used in rail service in load damping applications. They are superior to traditional elastomers primarily in their ease of fabrication. Like traditional elastomers they offer benefits including reduction in noise emissions and improved wear resistance in metal components that are in contact with such parts in the railcar suspension system. However, viscoelastic materials, such as the railroad bearing thermoplastic elastomer suspension element (or elastomeric pad), are known to develop self-heating (hysteresis) under cyclic loading, which can lead to undesirable consequences. Quantifying the hysteresis heating of the pad during operation is therefore essential to predict its dynamic response and structural integrity, as well as, to predict and understand the heat transfer paths from bearings into the truck assembly and other contacting components. This study investigates the internal heat generation in the suspension pad and its impact on the complete bearing assembly dynamics and thermal profile. Specifically, this paper presents an experimentally validated finite element thermal model of the elastomeric pad and its internal heat generation. The steady-state and transient-state temperature profiles produced by hysteresis heating of the elastomer pad are developed through a series of experiments and finite element analysis. The hysteresis heating is induced by the internal heat generation, which is a function of the loss modulus, strain, and frequency. Based on previous experimental studies, estimations of internally generated heat were obtained. The calculations show that the internal heat generation is impacted by temperature and frequency. At higher frequencies, the internally generated heat is significantly greater compared to lower frequencies, and at higher temperatures, the internally generated heat is significantly less compared to lower temperatures. However, during service operation, exposure of the suspension pad to higher loading frequencies above 10 Hz is less likely to occur. Therefore, internal heat generation values that have a significant impact on the suspension pad steady-state temperature are less likely to be reached. The commercial software package ALGOR 20.3TM is used to conduct the thermal finite element analysis. Different internal heating scenarios are simulated with the purpose of obtaining the bearing suspension element temperature distribution during normal and abnormal conditions. The results presented in this paper can be used in the future to acquire temperature distribution maps of complete bearing assemblies in service conditions and enable a refined model for the evolution of bearing temperature during operation.

Radiative Heat Transfer Analysis of Railroad Bearings for Wayside Hot-Box Detector Optimization

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.

Development of Prognostic Techniques for Surface Defect Growth in Railroad Bearing Rolling Elements

Prevention of bearing failures which may lead to catastrophic derailment is a major safety concern for the railroad industry. Advances in bearing condition monitoring hold the promise of early detection of bearing defects, which will improve system reliability by permitting early replacement of failing components. However, to minimize disruption to operations while providing the maximum level of accident prevention that early detection affords, it will be necessary to understand the defect growth process and try to quantify the growth speed to permit economical, non-disruptive replacement of failing components rather than relying on immediate removal upon detection. The study presented here investigates the correlation between the rate of surface defect (i.e. spall) growth per mile of full-load operation and the size of the defects. The data used for this study was acquired from defective bearings that were run under various load and speed conditions utilizing specialized railroad bearing dynamic test rigs operated by the University Transportation Center for Railway Safety (UTCRS) at the University of Texas Rio Grande Valley (UTRGV). Periodic removal and disassembly of the railroad bearings was carried out for inspection and defect size measurement and documentation. Castings were made of spalls using low-melting, zero shrinkage Bismuth-based alloys so that a permanent record of the full spall geometry could be retained. Spalls were measured using optical techniques coupled with digital image analysis and also with a manual coordinate measuring instrument with the resulting field of points manipulated in MatLab™ and Solidworks™. The spall growth rate in area per mile of full-load operation was determined and, when plotted versus spall area, clear trends emerge. Initial spall size is randomly distributed as it depends on originating defect depth, size, and location on the rolling raceway. The growth of surface spalls is characterized by two growth regimes with an initial slower growth rate which then accelerates when spalls reach a critical size. Scatter is significant but upper and lower bounds for spall growth rates are proposed and the critical dimension for transition to rapid spall growth is estimated. The main result of this study is a preliminary model for spall growth which can be coupled to bearing condition monitoring tools to permit economical scheduling of bearing replacement after the initial detection of spalls.

Models for the Residual Life of Railroad Bearing Grease in Laboratory and Industry Applications

Adequate lubrication in railroad bearings is crucial to the safe operation of these components. An investigation of the residual life of railroad bearing grease was conducted in a laboratory setting. The data was collected using a split-split-plot design of experiments. The Oxidation Induction Time (OIT), which is the time required for the remaining antioxidants in a sample of grease to be consumed in a test, is the response variable for this study. Low values of OIT indicate small remaining amounts of antioxidants in the grease and thus small remaining residual life in the grease. OIT measurements were made using differential scanning calorimetry. Laboratory testing was performed utilizing a specialized dynamic test rig that allowed four rail-road bearings of the same class, mounted on a test axle, to be subjected to varying operating conditions. The independent factors manipulated in this study were total service mileage, miles at load, average speed, mounted lateral spacing, and average temperature at three locations within each bearing. Additional information was recorded for each axle tested that includes axle number, bearing location within the test axle, grease location within each bearing and the presence or absence of a small sprall on the bearing surface. Regression analysis was employed to fit mixed effects models using JMP software. The first modeling effort was to develop the best possible model for laboratory usage. A second modeling effort was conducted to develop a model for industry usage without several variables available only in the laboratory setting. Web-based applications are provided for users to investigate the residual life of railroad bearing grease in both laboratory and industry settings.

Heat Generation in the Railroad Bearing Thermoplastic Elastomer Suspension Element

The main purpose of this ongoing study is to investigate the effect of heat generation within a railroad thermoplastic elastomer suspension element on the thermal behavior of the railroad bearing assembly. Specifically, the purpose of this project is to quantify the heat generated by cyclic loading of the elastomer suspension element as a function of load amplitude, loading frequency, and operating temperature. The contribution of the elastomer pad to the system energy balance is modeled using data from dynamic mechanical analysis (DMA) of the specific materials in use for that part. DMA is a technique that is commonly used to characterize material properties as a function of temperature, time, frequency, stress, atmosphere or a combination of these parameters. DMA tests were run on samples of pad material prepared by three different processes: injection molded coupons, transfer molded coupons, and parts machined from an actual pad. The results provided a full characterization of the elastic deformation (Energy Storage) and viscous dissipation (Energy Dissipation) behavior of the material as a function of loading frequency, and temperature. These results show that the commonly used thermoplastic elastomer does generate heat under cyclic loading, though the frequency which produces peak heat output is outside the range of common loading frequency in rail service. These results can be combined with a stress analysis and service load measurements to estimate internally generated heat and, thus, enable a refined model for the evolution of bearing temperature during operation.

Developing Empirical Models of Railroad Bearing Grease

The degradation of the grease used to lubricate railroad bearings is believed to be caused by two processes: the mechanical processes occurring within the bearing and a diffusion process. Appropriate lubrication of the bearings is critical during railroad service operation. The study presented here will focus on the development of empirical models that can accurately predict the residual useful life of railroad bearing grease. Modeling techniques to be employed include regression, regression trees and split plots. The data set used in the development of the model consists of more than 100 samples of grease that were taken from railroad bearings. The bearings have been subjected to experimental variables such as load conditions, rotational speed, temperature, and mileage all of which have been observed in a laboratory setting. The mileage parameter is consistent with the total miles that were run using the grease from which the sample has been taken. Load, speed, and temperature values fluctuate within the total service operation of the bearing; therefore, a high value, a low value, and a weighted average are taken for the aforementioned parameters. The grease samples are taken from critical locations of the bearing, the inboard raceway, the outboard raceway and the spacer ring area, meaning that there are three samples collected from each railroad bearing, each having their own set of corresponding parameters. The oxidation induction time (OIT) of the grease is an indicator of the residual life of the grease; therefore, the OIT for each sample had been acquired using a differential scanning calorimeter (DSC). OIT is dependent upon mileage, load, speed, and temperature. This study was successful in developing an empirical model which can be utilized to predict the residual life for given operational characteristics.