Cognitive Prediction-Enabled Online Intelligent Fault Diagnosis and Prognosis for Wind Energy Systems
Description: The objective of this research is to study the use of cognitive prediction paradigms for online fault diagnosis and prognosis to enable condition-based smart maintenance for wind energy systems. The approach is to: (1) study the use of time and frequency domain data mining methods to effectively extract the features of faults in a wind turbine from the signals acquired from the wind turbine condition monitoring system; and (2) study the use of artificial neural networks and machine learning for intelligently diagnosing and prognosing faults, predicting the lifetime, and quantitatively evaluating the physical condition of the wind turbine using the extracted fault features.
Intellectual Merit: This project will create innovative cognitive prediction-based models and computational algorithms to enhance condition awareness of geographically distributed wind energy systems. The findings of this research are highly transformable and will provide capabilities for enabling condition-based intelligent maintenance for other energy conversion and engineered systems.
Broader Impacts: The outcome of this project will further exploit the benefits of wind power by successfully reducing cost and improving reliability of wind energy systems and, therefore, will make wind energy a reliable, cost-competitive source of clean electricity. The increasing use of wind power will benefit various sectors of the nation's economy and contribute to sustainable development of society. Multiple fields covered by this project are areas where a talent shortage is projected in the United States, particularly in the Midwest. The proposed activities will provide a unique learning platform for young individuals to become skilled professionals.
Funder: National Science Foundation
Amount: $359,900
Funding type: grant
Funder scheme: Standard Grant
Start date: 2013-06-01
End date: 2017-05-31
Investigator: Wei Qiao (lead)
Institutions: University of Nebraska–Lincoln, University of Nebraska-Lincoln
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Funded outputs count: 22
Funder award ID: 1308045
OpenAlex ID: G4120103499