Wheelsets are removed from service for many reasons by North American Interchange Service. For example, journal bearings can be the cause of wheelset removal when operating temperature or acoustic signatures recorded by wayside detectors exceed certain limits. The Association of American Railroads (AAR) has established 13 failure progression modes to categorize the reason a bearing sets off one of these two detection systems. This study focuses on two failure progression modes: water etch caused by corrosion and issues associated with lubrication (grease). Greases for rail journal bearings are expected to satisfy a wide variety of requirements such as moisture tolerance, mechanical stability, vibration tolerance, range of operating temperatures and oxidation resistance, to name a few. This paper provides the reader an overview of several experiments and tests that were conducted with the goal of extending service life and reducing corrosion, including field tests used in the development of advanced journal bearing greases for the rail industry. One such new grease formulation was tested in the UK with good results.

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™ since a high-risk defect is 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. These wayside detectors are reactive in the detection of a defective bearing and require emergency stops in order to replace the wheelset containing the defective bearing. These costly and inefficient train stoppages can be prevented if a proper maintenance schedule can be developed at the onset of a defect initiating within the bearing. This proactive approach would allow for railcars with defective bearings to remain in service operation safely until reaching scheduled maintenance. Driven by the need for a proactive bearing condition monitoring system in the rail industry, the University Transportation Center for Railway Safety (UTCRS) research group 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 using temperature and vibration signatures 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 builds on previously published work that demonstrates the use of the advanced onboard condition monitoring system to identify defective bearings as well as the correlations developed for spall growth rates of defective bearing outer rings (cups). Hence, the system uses the root-mean-square (RMS) value of the bearing’s acceleration to assess its health. Once the bearing is determined to have a defective outer ring, the RMS value is then used to estimate the defect size. This estimated size is then used to predict the remaining service life of the bearing. The methodology proposed in this paper can prove to be a useful tool in the development of a proactive and cost-efficient maintenance cycle for railcar owners.

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 cm 2 (1 in 2 ) using the vibration signature, whereas, the temperature profile of that same bearing will indicate a healthy bearing that is operating normally.

In a typical railyard environment, a myriad of large and dynamic objects pose significant risks to railyard workers. Unintentional falls, trips and collisions with dynamic rolling stock due to distractions or lack of situational awareness are an unfortunate reality in modern railyards. The challenges of current technologies in detecting and tracking multiple differently-sized mobile objects in situations such as i) one-on-one, ii) many-to-one, iii) one-to-many, iv) blind spot, and v) interfering/non-interfering separation creates the possibility for reduction or loss of situational awareness in this fast-paced environment. The simultaneous tracking of assets with different size, velocity and material composition in different working and environmental conditions can only be accomplished through joint infrastructure-based asset discovery and localization sensors that cause no interference or impediment to the railyard workers, and which are capable of detecting near-misses as well. Our team is investigating the design and performance of such a solution, and is currently focusing on the innovative usage of lightweight low-cost RADAR under different conditions that are expected to be encountered in railyards across North America. We are employing Ancorteks 580-AD Software Defined RADAR (SDRadar) system, which operates at the license-free frequency of 5.8 GHz and with a variety of different configuration options that make it well-suited for generalized object tracking. The challenges, however, stem from the unique interplay between tracking large metallic objects such as railcars, locomotives, and trucks, as well as smaller objects such as railyard workers, in particular their robust discernment from each other. Our design’s higher-level system can interact with the lower-level SDRadar design to change the parameters in real-time to detect and track large objects over significant distances. The algorithm optimally adjusts waveform, sweep time and sample rate based on one or multiple detected object cross-sections and subsequently alters these parameters to be able to discern other objects from them that are in close proximity. We also use an ensemble method to determine the velocity and distance of target objects to accurately track the subject and larger objects at a distance. The methodology has been field-tested with several test cases in a multitude of weather and lighting conditions. We have also tested the proper height, azimuth and elevation angles for positioning our SDRadar to alleviate the risk of blind spots and enhancing the detection and tracking capabilities of our algorithm. The approach has outperformed our previous tests using visual and acoustic sensors for detection and tracking railroad workers in terms of accuracy and operating flexibility. In this paper, we discuss the details of our proposed approach and present our results from the field tests.

From the original “steam trumpet” built for locomotives in 1832 by the Leicester and Swannington Railway to modern air-pressure horns, train warning signals have not changed significantly in nearly 200 years. The effectiveness of train warning signals has been of particular concern for trespassers listening to music with headphones. The authors have conducted research as part of a Federal Railroad Administration program to design and assess the effectiveness of candidate new emergency warning signal (EWS) sounds. This paper summarizes a literature review to understand the needs for a new EWS sound and principles of audible signal detection. Acoustic measurements were conducted of headphones to understand in-ear music levels and active and passive sound attenuation. Candidate EWS sounds were developed with a goal of maintaining the identification of a train approaching and increasing the sense of urgency and response time for trespassers to vacate the tracks. Testing of candidate EWS sounds was conducted in an audio booth and on-board a moving locomotive. The research results have shown that a new EWS sound can maintain the association of a train approaching, increase the sense of urgency, reduce the reaction time for trespassers to vacate the tracks and improve safety on railroad corridors.

Two major components of rolling stock that are always of great interest when it comes to maintenance and safety related issues are car wheels and bearings. Rail car wheels are subjected to a variety of damage types due to their interaction with the track and brakes. It is important for the rail industry to detect these defects and take proper action at an early stage, before more damage can be caused to the train or possibly the track and to prevent possible safety hazards. Different inspection sensors and systems, such as wheel impact monitors, wheel profile detectors, hotbox detectors and acoustic detection technologies, are employed to detect different types of wheel and bearing defects. Usually no single sensor can accurately detect all kinds of damages and hence a combination of different sensors and systems and manual inspection by experts is used for wheel maintenance purposes and to guarantee train safety. The more complete and accurate the automatic defect detections are, the less manual examination is necessary, leading to potential savings in inspection time/resources and rail car maintenance costs. Wayside thermal and visible spectrum cameras are one option for the automatic wheel and bearing inspection. Each of these sensors has their own strengths and weaknesses. There are some types of defects that are not detectable at an early stage in the images taken by a vision camera, however these defects generate a distinctive heat pattern on the wheel or bearing that is clearly visible in the thermal imagery. On the other hand, other damages might be detectable from the visible spectrum image, but not necessarily have a distinguishable heat pattern in the thermal imagery. Since a thermal image is basically built of solely temperature data, it excludes other critical information, such as texture or color. This makes thermal and visible spectrum imagery complementary and if the images are fused the result will benefit from the strengths of both sensors. In this paper, wavelet decomposition is employed to extract the features of the thermal and vision imagery. Then the two images are merged based on their decompositions and a fused image is composed. The resulting fused image contains more information than each individual image and can be used as an input for image-based wheel and bearing defect detection algorithms. To verify the proposed method and to show an example of this application, it is demonstrated on a real data set from a Union Pacific rail line to identify sliding wheels.

It is estimated that up to 9.3 million persons may be impacted by locomotive horn noise and up to 4.6 million of those may be severely impacted. 1 In 2009, there were over 1,900 incidents, over 700 injuries and over 240 fatalities at highway-rail grade crossings. 2 The National Academy of Engineering Committee on Technology for a Quieter America has indicated that the public would benefit if train warning horns were more directional and has recommended that research and development be undertaken to better understand the effects on safety and benefits to the public. 3 A directive train horn has the potential to focus audible warning signals to desired locations including pedestrians and motorists at highway-rail grade crossings while minimizing noise to the surrounding community and employees in the locomotive cab. As a part of an ongoing Federal Railroad Administration (FRA)-sponsored research and development effort, the authors have examined the feasibility of and recommended an acoustical specification for an optimized train horn that would improve the detectability of the warning signal for motorists at critical positions along the crossing road while reducing the area of environmental noise impact. The detectability, noise impact area and occupational noise exposure have been compared for the optimized horn and several typical standard horns. Near the beginning of most sounding events (1/4-mile from the grade crossing) the optimized horn reduces noise exposure because a narrow beam of sound can be generated and focused at the grade-crossing. As the train approaches the crossing, the beam width must become wider. It is found that detectability could be improved and noise impact area reduced by up to 57%, but the optimized horn must have a directivity pattern and amplitude that dynamically changes as a function of train position relative to the crossing. Current acoustic source technologies which generate directive sound were examined including “acoustic hailing devices” (AHDs) which are recent technological advancements typically used for naval communications. Capable of focusing high amplitudes of sound within a narrow beam and dynamically changing the directivity pattern through electronic beam steering, AHDs have been identified as a feasible means of meeting the required specifications. A critical design issue for the optimized horn is controlling the directivity pattern at low frequencies. Development and testing of a prototype is in progress and actual improvements to detectability and reductions in noise impact will be analyzed. The paper briefly discusses the feasibility of the optimized horn and general information on cost and implementation.

This work presents a generic passive non-contact based acoustic health monitoring approach using ultrasonic acoustic emissions (UAE) to facilitate classification of bearing health via neural networks. This generic approach is applied to classifying the operating condition of conventional ball bearings. The acoustic emission signals used in this study are in the ultrasonic range (20–120 kHz), which is significantly higher than the majority of the research in this area thus far. A direct benefit of working in this frequency range is the inherent directionality of microphones capable of measurement in this range, which becomes particularly useful when operating in environments with low signal-to-noise ratios that are common in the rail industry. Using the UAE power spectrum signature, it is possible to pose the health monitoring problem as a multi-class classification problem, and make use of a multi-layer artificial neural network (ANN) to classify the UAE signature. One major problem limiting the usefulness of ANN’s for failure classification is the need for large quantities of training data. This becomes a particularly important issue when considering applications involving higher value components such as the turbo mechanisms and traction motors on diesel locomotives. Artificial training data, based on the statistical properties of a significantly smaller experimental data set is created to train the artificial neural network. The combination of the artificial training methods and ultrasonic frequency range being used results in an approach generic enough to suggest that this particular method is applicable to a variety of systems and components where persistent UAE exist.

Shelling in rails compromises detection of transverse defects, since ultrasonic energy cannot be sufficiently coupled through this surface condition. This paper describes a method that utilizes ultrasonic guided waves generated in the rail using electro-magnetic acoustic transducers (EMATs) to detect defects under shelling. The EMATs travel along the rail head and introduce ultrasonic energy into the rail head. The ultrasound penetrates underneath surface shelling and engine burns to detect hidden transverse flaws that may be missed by tradition rail flaw inspection technology. Software with artificial intelligence capabilities was also developed in which pattern recognition classifiers are used to classify defects in real-time. Field tests were conducted to determine the sensitivity of the method to transverse flaws and it was seen that defects as small as 5% cross-section head area (CSHA) in size could be detected using this method.

The noisy and monotonous environment of a locomotive cabin is certainly not conducive to clear communication or to high levels of crew alertness. Both of these problems have a negative impact on the operational safety of the locomotive. In this paper, an acoustic beamforming system is developed and evaluated for enhancing the speech signals measured in a locomotive cabin. Higher signal-to-noise ratio (SNR) speech signals can significantly improve communication as well as enable the use of speech-based alertness detection methods. Acoustic beamforming utilizes a spatially distributed array of low-cost microphones permanently mounted in the locomotive cabin trim, with a digital signal processing algorithm to effectively create a focused directional microphone which can be pointed at any crew member in the cabin. Preliminary experimental testing of this system in a green truck cabin has been successful, even with very high levels of noise.

Trackside Acoustic Detection System (TADS ® ) development spearheaded implementation of an acoustic freight car roller bearing detector whose purpose is to prevent in-service bearing failures (burned off journals and hot bearing detector train stops). The means of accomplishing this goal is by providing the user with a warning of internal bearing defects or degradation with component involvement and severity information. The Transportation Technology Center, Inc. (TTCI) began the TADS ® development process in 1994 with basic research into bearing defect acoustic emissions. Subsequently, TTCI conducted prototype testing on a North American railroad, constructed and installed of several international beta test systems, and finally has sold production systems in North America and internationally. There are currently about 40 TADS ® sites in operation world-wide with 2.0 or more systems scheduled for installation in 2007. The original mission for TADS ® in North America was an early warning of bearing degradation to allow for scheduled maintenance, but after initial evaluation, this mission enlarged to include notification of potentially high risk bearings. The high risk bearing is defined as one with fairly large areas of internal damage and at an increased risk of overheating or failing in service. The high risk bearing has a different acoustic signature, dissimilar to that of smaller defects. This paper will outline the change in mission for this detector and describe the development of an improved capability for detecting these high risk bearings.

The interaction of machine components under loading is an integral part of railway engineering design. It results in a mechanical contact and associated stress distribution, which may be an initiation point for failure modes such as fatigue, fretting or wear. In this study the reflection of ultrasound is used to investigate how real engineering components contact. It is a non-intrusive technique preserving the mechanics of the contact. The concept is simple; an acoustic wave bounces back from an incomplete interface. The higher the contact load, the more conformal will be the contact and hence more of the wave will be transmitted. A spring model is used to determine maps of contact stiffness from contact ultrasonic reflection data. A calibration procedure is then used to determine the pressure. The technique has been used to study two railway engineering applications. The first was a wheel/rail contact. Measurements were taken using specimens cut from sections of wheel and rail. Both the tread/railhead and flange/gauge corner contacts were studied. The second was a wheel hub/axle interference fit. An actual wheelset was used, with a hollow axle to allow access for the ultrasonic transducer.