A Condition Monitoring and Fault Isolation System for Wind Turbine Based on SCADA Data

Abstract

Condition monitoring of wind turbines based on SCADA data faces challenges such as low sampling rates, time-varying working conditions, and a lack of historical fault data. This paper introduces a novel system that uses covariate-adjusted preprocessing and variable selection methods to monitor overall health and isolate faults without relying on expert knowledge or historical fault data.

Juan Du

Assistant Professor

My research interests include knowledge-infused data science for quality improvement, industrial data analytics and machine learning, and system informatics and control for manufacturing applications.