Optimizing a Railroad Bearing Condition-Monitoring Algorithm for Use with an Onboard Wireless Low-Power Sensor Module

An algorithm that utilizes vibration measurements was developed by the UTRGV Center for Railway Safety to monitor the condition of railroad bearings. This algorithm uses the data collected from accelerometers on the bearing adapters to determine if there is a defect, where the defect is within the bearing, and the approximate size of the defect. Laboratory testing was performed on the UTCRS single bearing test rig. A four-second sample window of the recorded vibration data is used by the algorithm to reliably identify the defective component inside the bearing with up to a 100% confidence level. However, considerable computational power is used to analyze the 20,480 data points. Consequently, if this condition monitoring algorithm is to be implemented on a wireless module, the battery life becomes restricted. Reducing the sample window to one second of data collected would conserve energy but might sacrifice some accuracy in the analysis. To that end, a wireless onboard condition monitoring module that collects one second of vibration data (5,120 data points) was fabricated and tested to compare its efficacy against the existing wired setup. The study presented here demonstrates that the optimized algorithm for the wireless system can reliably identify the bearing condition with negligible compromise to accuracy and lower power consumption

Estimating the Inner Ring Defect Size and Residual Service Life of Freight Railcar Bearings Using Vibration Signatures

There are currently two primary wayside detection systems for monitoring the health of freight railcar bearings in the railroad industry: The Trackside Acoustic Detection System (TADS™) and the wayside Hot-Box Detector (HBD). TADS™ uses wayside microphones to detect and alert the train operator of high-risk defects. However, many defective bearings may never be detected by TADS™ since a high-risk defect is a spall which spans about 90% of a bearing’s raceway, and there are less than 30 systems in operation throughout the United States and Canada. HBDs sit on the side of the rail-tracks and use non-contact infrared sensors to acquire temperatures of bearings as they roll over the detector. These wayside bearing detection systems are reactive in nature and often require emergency stops in order to replace the wheelset containing the identified defective bearing. Train stoppages are inefficient and can be very costly. Unnecessary train stoppages can be avoided if a proper maintenance schedule can be developed at the onset of a defect initiating within the bearing. Using a proactive approach, railcars with defective bearings could be allowed to remain in service operation safely until reaching scheduled maintenance. The University Transportation Center for Railway Safety (UTCRS) research group at the University of Texas Rio Grande Valley (UTRGV) has been working on developing a proactive bearing condition monitoring system which can reliably detect the onset of bearing failure. Unlike wayside detection systems, the onboard condition monitoring system can continuously assess the railcar bearing health and can provide accurate temperature and vibration profiles to alert of defect initiation. 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 onboard condition monitoring system to identify defective bearings as well as the correlations developed for spall growth rates of defective bearing outer rings (cups). The system first uses the root-mean-square (RMS) value of the bearing’s acceleration to assess its health. Then, an analysis of the frequency domain of the acquired vibration signature determines if the bearing has a defective inner ring (cone) and the RMS value is used to estimate the defect size. This estimated size is then used to predict the residual life of the bearing. The methodology proposed in this paper can assist railroads and railcar owners in the development of a proactive and cost-efficient maintenance cycle for their rolling stock

Estimating the Outer Ring Defect Size and Remaining Service Life of Freight Railcar Bearings Using Vibration Signatures

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

Optimization of Railroad Bearing Health Monitoring System for Wireless Utilization

In the railroad industry, systematic health inspections of freight railcar bearings are required. Bearings are subjected to high loads and run at high speeds, so over time the bearings may develop a defect that can potentially cause a derailment if left in service operation. Current bearing condition monitoring systems include Hot-Box Detectors (HBDs) and Trackside Acoustic Detection Systems (TADS™). The commonly used HBDs use non-contact infrared sensors to detect abnormal temperatures of bearings as they pass over the detector. Bearing temperatures that are about 94°C above ambient conditions will trigger an alarm indicating that the bearing must be removed from field service and inspected for defects. However, HBDs can be inconsistent, where 138 severely defective bearings from 2010 to 2019 were not detected. And from 2001 to 2007, Amsted Rail concluded that about 40% of presumably defective bearings detected by HBDs did not have any significant defects upon teardown and inspection. TADS™ use microphones to detect high-risk bearings by listening to their acoustic sound vibrations. Still, TADS™ are not very reliable since there are less than 30 active systems in the U.S. and Canada, and derailments may occur before bearings encounter any of these systems. Researchers from the University Transportation Center for Railway Safety (UTCRS) have developed an advanced algorithm that can accurately and reliably monitor the condition of the bearings via temperature and vibration measurements. This algorithm uses the vibration measurements collected from accelerometers on the bearing adapters to determine if there is a defect, where the defect is within the bearing, and the approximate size of the defect. Laboratory testing is performed on the single bearing and four bearing test rigs housed at the University of Texas Rio Grande Valley (UTRGV). The algorithm uses a four second sample window of the recorded vibration data and can reliably identify the defective component inside the bearing with up to a 100% confidence level. However, about 20,000 data points are used for this analysis, which requires substantial computational power. This can limit the battery life of the wireless onboard condition monitoring system. So, reducing the vibration sample window to conduct an accurate analysis should result in a more optimal power-efficient algorithm. A wireless onboard condition monitoring module that collects one second of vibration data (5,200 samples) was manufactured and tested to compare its efficacy against a wired setup that uses a four second sample window. This study investigates the root-mean-square values of the bearing vibration and its power spectral density plots to create an optimized and accurate algorithm for wireless utilization