SCADA Data

This paper proposes a covariate-regulated sparse subspace learning (CSSL) model to address the challenges of complex, time-varying cross-correlation in multivariate functional data, and demonstrates its application in process monitoring and fault isolation using SCADA data from wind turbines.

This article develops a novel condition monitoring and fault isolation system for wind turbines using SCADA data, addressing challenges such as low sampling rates, time-varying working conditions, and a lack of historical fault data.