Evaluating covariance in prognostic and system health management applications
Simulated noisy data sets are used to compare the accuracy of four existing covariance estimation methodologies Among the discussed methodologies the NNVE algorithm provides the most accurate estimates of covariance. To further improve the accuracy of the covariance estimation, a new methodology based on a modification of the NNVE methodology is proposed. The proposed methodology is shown to exhibit improved performance in classification as well as anomaly detection applications.
prognostics system health management covariance estimation