At the Turbo Expo 2018: Turbomachinery Conference & Expedition, in Oslo, Norway, an innovative approach for assessing operating and near real-time data from power generating assets with meaningful predictive analytics was presented and discussed. GT2018-75030, entitled; Energy Innovation: A Focus on Power Generation Data Capture & Analytics in a Competitive Market established a challenging objective for the industry:

“To advance the notion that the fusion of total plant data, from three primary sources, with the ability to transform, analyze, and act based on integrating subject matter expertise is essential for effectively managing assets for optimum performance and profitability; executing and delivering on the promise of “Big Data” and advanced analytics.”

Throughout 2019 and 2020, a team comprised of members from Strategic Power Systems, Inc. ® (SPS), Turbine Logic (TL), and two National Labs; National Energy Technology Laboratory (NETL) and Oak Ridge National Laboratory (ORNL), collaborated on the paper’s hypothesis. The team worked with the support of funding from DOE’s Fossil Energy Program through its HPC4 Materials Program, which provided access to the High-Performance Computing assets at both laboratories. The team brought unique skills, strengths, and capabilities that would serve as the basis for an effective, open, and challenging collaboration. The engineering and data science disciplines that converged on this project provided the back-bone for the unbiased analysis and model building that took place; relying on a unique and up-to-date source of plant operating and design data essential for performing the engineering scope of work. A key objective was to use the data and the modeling to be predictive; to characterize remaining life, expended life, and to determine the “next failure” for critical systems and components.

Proof-of-concepts were tested for longer term, data-driven reliability prediction for fleets of power generating assets, near real-time prediction of power plant faults which could lead to imminent failure, and physics-based model prediction of life consumption of critical parts. Each of these pilot scale projects is summarized with key results presented.

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