Automation IIoT

Duke Power Optimizes PdM Service

Grant Gerke | December 19, 2017

Duke Energy Renewables division manages or owns more than 4,000 MW of wind power in six states in the United States.
Duke Energy Renewables division manages or owns more than 4,000 MW of wind power in six states in the United States.

Maintenance is a pure cost for manufacturers, food producers, processors, and utilities. Due to this persistent fact, changes or investments in maintenance approaches come slowly, but advanced predictive- and condition-monitoring technologies are pushing manufacturing leaders to new thoughts on “fixed costs.”

Internal company debates on transitioning to predictive maintenance (PdM) include management buy-in, project scopes, workforce strategies, and, of course, return-on-investment (ROI). Over the past two years, this column has shown how first-movers have been implementing smaller pilot projects to provide management with quick results while keeping workforce demands to a minimum.   

However, early adopters are examining new approaches—after evaluating IIoT (Industrial Internet of Things) pilots—with remote monitoring services or by leveraging predictive platforms. Price Waterhouse Cooper’s 2016 Global Industry 4.0 Survey showed “early adopters are expecting to forecast revenue gains of more than 30% and greater than 30% in cost reductions at the same time.”

While this PWC survey isn’t speaking directly to optimizing revenues or new business outcomes resulting from IIoT investments, utility companies such as Duke Energy Corp., Charlotte, NC (duke-energy.com), are evaluating how PdM investments could lead to this scenario.

Similar to General Electric’s model of testing its new monitoring platform on their assets, Duke Energy recently announced a partnership with Sentient Science, Buffalo, NY (sentientscience.com), to implement a “digitalization strategy to continue to safely manage and reduce the cost of maintaining Duke Energy’s wind turbines.”

Duke Energy Renewables, owner/manager of more than 4,000 MW of wind assets, now uses Sentient Science’s DigitalClone Live software to diagnose gearbox failures and provide corrective actions for wind turbines. The software helps identify and predict when cracks will appear in the microstructure of rotating mechanical components.

“We want to ensure that our technicians and asset managers have state-of-the-art tools,” stated Jeff Wehner, vice president , Operations, Renewables at Duke Energy. “After we have some experience in our fleet, we expect to offer this enhanced service to other operators who contract with us.”

The utility giant recently used the predictive software to analyze 109 Winergy, 4410.4 GE 1.5-MW-rated gearbox machines and benchmarked these findings against its internal monitoring tools, such as oil debris and vibration monitoring. Winergy Drive Systems Corp. is located in Elgin, IL (winergy-group.com).

The monitoring software indicated that 11 specific wind turbines would show gearbox damage in the next 12 months, according to Sentient Science. Duke Energy Renewables service personnel climbed towers and did borescope examinations and confirmed that all seven gearbox inspections had observable damage. The software also correctly identified the damaged subcomponents on five turbines.

To learn more about IIoT applications, click here. EP

ggerke@efficientplantmag.com

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Grant Gerke

Grant Gerke

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