Poseidon Systems is deploying its wear debris condition monitoring device, the DM4500, across 10 sites in Duke Energy Sustainable Solutions’ wind turbine fleet.
The sites include wind turbines from Siemens Gamesa, Suzlon, Nordex, and GE wind turbines, a total of 402 turbines.
“We appreciate Duke Energy Sustainable Solutions’ forward thinking and innovation integrating wear debris CMS into their already existing vibration-based CMS monitoring program,” said Mark Redding, CEO and founder of Poseidon Systems.
Poseidon’s monitoring is being deployed alongside Duke’s existing vibration-based condition monitoring system to provide earlier detection of gearbox faults, better indicators of fault severity and end-of-life, as well as catastrophic event detection.
“The addition of the Poseidon Wear Debris Monitor Device to our toolbox will greatly improve our ability to understand the current condition or status of our gearboxes,” said Jeffrey Wehner, Duke Energy Sustainable Solutions’ Vice President of Operations. “This device, coupled with vibration and lubrication analyses, will allow the team to provide more specific recommendations to extend gearbox life. Being able to predict future gearbox events and plan repairs or replacements prior to catastrophic failure will result in cost savings to the business unit.”
Poseidon Systems projects that it will have 10,000 wind-turbine condition monitoring kits installed by end of 2021.
Using data from the Poseidon Systems’ installed base, Poseidon will continue to work with Duke Energy Sustainable Solutions and other wind customers to develop more advanced detection alarms and life extension algorithms. This development has extended to recent integration of journal bearings on wind-turbine gearboxes, using the DM4500’s superior detection range of non-ferrous materials for better detection of emerging faults.
Through Poseidon Live (PSL), cloud-based online monitoring software, Poseidon Systems is helping customers detect asset failures at the earliest possible point in time and prevent failures through identification of root causes before failure occurs.
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