Enhancing the Prediction of Mach Number in Wind Tunnel With a Regression-Based Outlier Detection Framework

Abstract

Model predictive control (MPC) has been successfully applied in practical wind tunnel systems. As a critical component of MPC, the accurate prediction of Mach number becomes hence very significant. Real measurement data records often contain outliers or noisy data because of the harsh working conditions of practical systems. Such datasets are detrimental to most data-driven Mach number predictors. This paper focuses on improving Mach number prediction via a regression-based outlier detection framework. The development of a regression-based outlier detector for enhancing Mach number prediction is natural since only regression analysis needs to be implemented for both prediction and outlier detection. In contrast to existing regression-based detectors, we have taken into account three problems that may greatly impact the detecting performance. Specifically, we use Bagging technique to reduce the negative influence of unknown outliers during the training phase. Then, the notion of extreme value analysis is used to determine an appropriate threshold for the calculated outlier scores. A robust scaling method based on Hampel identifier is also used to alleviate the influence of outliers on data scaling. Several datasets stemming from a real-world wind tunnel are used in experiments to verify the effectiveness of our detector. The experimental results show that regression-based outlier detectors often perform better if the prior knowledge about the dependent variable is used. In addition, the proposed detector has outperformed other regression-based detectors.

Taxonomy of outlier detection methods for power system measurements

“…However, it needs prior knowledge of the percentage of contamination present in data [34]. On the other hand, the Hampel Identifier Method proposed in [35] is very similar to the 3-sigma method. Instead of the sample mean, it utilizes sample median to find the deviation.…”