Vibration-Based Defect Detection for Freight Railcar Tapered-Roller Bearings

The railroad industry currently utilizes two wayside detection systems to monitor the health of freight railcar bearings in service: The Trackside Acoustic Detection System (TADS™) and the wayside Hot-Box Detector (HBD). TADS™ uses wayside microphones to detect and alert the conductor of high risk defects. Many defective bearings may never be detected by TADS™ due to the fact that a high risk defect is considered a spall which spans more than 90% of a bearing’s raceway, and there are less than 20 systems in operation throughout the United States and Canada. Much like the TADS™, the HBD is a device that sits on the side of the rail tracks and uses a non-contact infrared sensor to determine the temperature of the train bearings as they roll over the detector. The accuracy and reliability of the temperature readings from this wayside detection system have been concluded to be inconsistent when comparing several laboratory and field studies. The measured temperatures can be significantly different from the actual operating temperature of the bearings due to several factors such as the class of railroad bearing and its position on the axle relative to the position of the wayside detector. Over the last two decades, a number of severely defective bearings were not identified by several wayside detectors, some of which led to costly catastrophic derailments. In response, certain railroads have attempted to optimize the use of the temperature data acquired by the HBDs. However, this latter action has led to a significant increase in the number of non-verified bearings removed from service. In fact, about 40% of the bearings removed from service in the period from 2001 to 2007 were found to have no discernible defects. The removal of non-verified (defect-free) bearings has resulted in costly delays and inefficiencies. Driven by the need for more dependable and efficient condition monitoring systems, the University Transportation Center for Railway Safety (UTCRS) research team at the University of Texas Rio Grande Valley (UTRGV) has been developing an advanced onboard condition monitoring system that can accurately and reliably detect the onset of bearing failure. The developed system currently utilizes temperature and vibration signatures to monitor the true condition of a bearing. This system has been validated through rigorous laboratory testing at UTRGV and field testing at the Transportation Technology Center, Inc. (TTCI) in Pueblo, CO. The work presented here provides concrete evidence that the use of vibration signatures of a bearing is a more effective method to assess the bearing condition than monitoring temperature alone. The prototype bearing condition monitoring system is capable of identifying a defective bearing with a defect size of less than 6.45 cm2 (1 in2) using the vibration signature, whereas, the temperature profile of that same bearing will indicate a healthy bearing that is operating normally

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

Fatigue Life Estimation of Modified Railroad Bearing Adapters for Onboard Monitoring Applications

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

Structural integrity of conventional and modified railroad bearing adapters for onboard monitoring

This paper presents a detailed study of the structural integrity of conventional and modified railroad bearing adapters for onboard monitoring applications. Freight railcars rely heavily on weigh bridges and stations to determine cargo load. As a consequence, most load measurements are limited to certain physical railroad locations. This limitation provided an opportunity for an optimized sensor that could potentially deliver significant insight on bearing condition monitoring as well as load information. Bearing adapter modifications (e.g. cut outs) were necessary to house the sensor and, thus, it is imperative to determine the reliability of the modified railroad bearing adapter, which will be used for onboard health monitoring applications. To this end, this study quantifies the impact of the proposed modifications on the adapter structural integrity through a series of experiments and finite element analyses. The commercial software Algor 20.3TM is used to conduct the stress finite element analyses. Different loading scenarios are simulated with the purpose of obtaining the conventional and modified bearing adapter stresses during normal and abnormal operating conditions. This information is then used to estimate the lifetime of these bearing adapters. Furthermore, this paper presents an experimentally validated finite element model which can be used to attain stress distribution maps of these bearing adapters in different service conditions. The maps are also useful for identifying areas of interest for an eventual inspection of conventional or modified railroad bearing adapters in the field

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

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

Board 399: The Freshman Year Innovator Experience (FYIE): Bridging the URM Gap in STEM

The project focuses on increasing “effective STEM education and broadening participation” in underrepresented minority STEM students at the University of Texas Rio Grande Valley (UTRGV) to successfully face academic and professional challenges, recently exacerbated by the COVID-19 pandemic. The Freshman Year Innovator Experience proposes the development of self-transformation skills in freshman mechanical engineering students to successfully face academic and professional challenges exacerbated by the COVID-19 pandemic while working on two parallel projects of technical design innovation and academic career pathways. The authors will present the work in progress and preliminary results from a pilot implementation of the Freshman Year Innovator Experience. This project is funded by NSF award 2225247

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

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