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: 
  • Accident prevention through early detection of impending failures,
  • Reduced operating costs through fewer stoppages, and more efficient and effective replacement and maintenance schedules, and
  • Creation of a large-scale database of bearing incidents, enabling further research.
Deliverables will include a set of working prototype modules for one railcar, a compact analysis module (CAM), design information including schematics, signal processing parameters, and algorithms, and comprehensive test data. System validation and verification will be carried out through extensive laboratory testing.
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