Monitoring wear with integrated ultrasonic transducers for predictive maintenance in mining

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Proceedings titleFirst International Seminar on Mining Plant and Equipment Maintenance: Maplemin 2014: Proceedings
ConferenceMaplemin 2014 - First International Seminar on Mining Plant and Equipment Maintenance, July 2-4, 2014, Lima, Peru
AbstractMaintenance and downtime costs can take a significant part of the cost of mining operation. Additionally, unplanned downtime could cost several times as much as scheduled downtime. Real time measurement of wear and corrosion can provide crucial information needed to determine an optimal schedule for maintenance to maximise equipment availability while ensuring its reliability. It also leads to savings from optimized spare parts handling and helps minimize costs associated with unnecessary preventative maintenance and negative impact on safety and the environment caused by unexpected equipment failures. This paper presents a technology of “painted-on” ultrasonic transducers that can be integrated into structures to be monitored and accurately measure wear or corrosion induced structure material losses through ultrasonic thickness measurement. These transducers have a small footprint, performance comparable to other commercially available ultrasonic transducers, and can sustain temperatures as high as 400 ºC. They are part of the next generation condition-based maintenance that embraces new advanced sensors connected to wireless network and advanced algorithms to provide a powerful tool for maintenance scheduling and consequently an enormous potential for cost savings. The application of this technology to mining equipment and its synergy with other sensors are discussed. A vision of next generation assets management that includes predictive maintenance enabled by wide use of innovative, low cost and wireless sensors is also discussed.
Publication date
AffiliationNational Research Council Canada; Energy, Mining and Environment
Peer reviewedNo
NRC numberNRC-EME-55642
NPARC number21272072
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Record identifierff283a85-a06a-4409-b791-1a45f5d82a0d
Record created2014-06-18
Record modified2016-05-09
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