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Lindy Williams is a member of the Data, Analysis, and Visualization Group within the Computational Science Center. Her work is focused around using statistical modeling to understand, predict, or determine events in many different applications across the lab, leveraging her experience in big data when necessary. Some areas of application for her work within renewable energy research include transportation, wind, and solar. Lindy enjoys using her experience in math and statistics to help projects better understand their data and the processes producing that data.

Research Interests

Timeseries forecasting

Statistical modeling

Data analytics

Education

B.A., Mathematics, University of Colorado at Colorado Springs

M.S., Statistics, Colorado School of Mines

Featured Work

Scalable Wind Turbine Generator Bearing Fault Prediction Using Machine Learning: A Case Study, IEEE International Conference on Prognostics and Health Management (2020)

A Data-driven Operational Model for Traffic at Dallas Fort-Worth International Airport, 99th Transportation Research Board Annual Meeting (2020)

A Methodology for Reliability Assessment and Prognosis of Bearing Axial Cracking in Wind Turbine Gearboxes, Renewable and Sustainable Energy Reviews (2020)

Prognosis of Wind Turbine Gearbox Bearing Failures using SCADA and Modeled DataAnnual Conference of the Prognostics and Health Management Society (2020)

Awards and Honors

2020 ICPHM Finalist for Best Industry Paper: Scalable Wind Turbine Generator Bearing Fault Prediction Using Machine Learning: A Case Study