Low Power Wireless Sensors for Railroad Bearing Health Monitoring
| University | The University of Texas Rio Grande Valley (UTRGV) |
| Principal Investigators | Heinrich Foltz, Ph.D., P.E., Electrical Engineering (PI) Constantine Tarawneh, Ph.D., Mechanical Engineering (Co-PI) |
| PI Contact Information | Electrical Engineering EENGR 3.225 Dept. (956) 665-2609 Office (956) 665-5016 heinrich.foltz@utrgv.edu |
| Funding Source(s) and Amounts Provided (by each agency or organization) | Federal Funds (US DOT UTC Program): $63,389 Cost Share Funds (UTPA): $22,477 |
| Total Project Cost | $85,866 |
| Agency ID or Contract Number | DTRT13-G-UTC59 |
| Start and End Dates | February 2017 – August 2018 |
| Brief Description of Research Project | We propose to develop an integrated, self-powered condition monitoring system for rail vehicle bearings. This system will leverage previous research at UTCRS on damage detection sensors and algorithms, service life prediction, signal processing for rail environments, and energy harvesting. Our UTRGV railroad research group has already shown that a combination of vibration, temperature, and load sensing can detect bearing defects and damage at a very early stage, and before it becomes a safety hazard, enabling selective preventive replacement during planned maintenance stops. The research group has also demonstrated signal processing electronics that allow detection of critical signals in challenging noise environments. The developed system will consist of self-powered miniature modules, directly mounted on bearing adapters, which will monitor the vibration spectrum, temperature history, and total applied vertical load on the bearing, and wirelessly transmit the data to a compact analysis module located on the rail vehicle. While there have been similar systems, ours would be unique in: (a) incorporation of our signal processing electronics that have proven success in extracting usable data from high noise situations, (b) a combination of vibration, temperature, and load sensors and sampling rates that have been optimized in both laboratory and field testing environments, (c) initial data analysis at the bearing sensor level to determine whether further analysis is warranted, (d) spectrum analysis algorithms, embedded in the compact analysis module, that have demonstrated superior performance in detecting and classifying bearing defects, and (e) self-powering using energy harvesting techniques.Benefits of the proposed system include the following:
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| Keywords | Railway safety, railroad bearing health monitoring, bearing condition monitoring, wireless sensors, low-power sensors, compact analysis module, bearing-level sensor module |
| Describe Implementation of Research Outcomes (or why not implemented) Place Any Photos Here | Pending Project Completion. |
| Impacts/Benefits of Implementation (actual, not anticipated) | Pending Project Completion. |
| Report | http://www.utrgv.edu/railwaysafety/_files/documents/research/mechanical/utcrs-utrgv-research-2017cy-wireless-sensors-for-railroad-bearing-health-monitoring.pdf |
| Project Website | http://www.utrgv.edu/railwaysafety/research/mechanical/2017/wireless-sensors-for-railroad-bearing-health-monitoring/index.htm |