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Power generation involves a complicated systems of systems which depend on predictive maintenance to operate productively and within O&M budgets. But existing analytic methods make it difficult to identify suboptimal conditions or unique failure inducing behaviors and require significant effort and resources to maintain over time. With the rise of digitization and new asset technologies, how can maintenance teams take advantage of these changing industrial dynamics?

Digitization and advanced analytics present a significant opportunity to leverage asset data and improve power generating operations. Machine learning solutions are used in predictive maintenance to extract important machine insights automatically and amplify the value of subject matter experts. This webinar will provide an overview of how machine learning can be used to improve productivity and walks through several customer case studies that illustrate these concepts in action.

Attendees will learn:

1. What solution components are necessary for a machine learning approach to work well in their industrial operation
2. How machine learning supplements and fits into existing predictive maintenance strategies at the asset and plant-wide level
3. How machine learning predictive maintenance solutions can be implemented to drive short and long-term productivity

Slavek Zaremba
Sr. Director & Chief Architect IIoT @SparkCognition
Slavek Zaremba is Sr. Director and Chief Architect for Industrial IoT solutions at SparkCognition where he is leveraging his 20+ years of experience in the area of power generation and utilities. Before joining SparkCognition he worked for 8 years at FPL and NextEra Energy Resources, where he was responsible for driving new technology and daily operations of NextEra’s remote monitoring and operations teams spanning all of the company’s generation equipment (including combined-cycle, fossil, hydro, nuclear, wind, solar, and battery storage, over 120 sites, 40GW). Before that he worked for 12 years at GE Energy with the focus on predictive analytics, remote monitoring & diagnostics, and implementation of AI technologies to power generation and smart grid operations.

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