Study on Statistical Outlier Detection and Labelling

Outliers accompany control engineers in their real life activity. Industrial reality is much richer than elementary linear, quadratic, Gaussian assumptions. Outliers appear due to various and varying, often unknown, reasons. They meet research interest in statistical and regression analysis and in data mining. There are a lot of interesting algorithms and approaches to outlier detection, labelling, filtering and finally interpretation. Unfortunately, their impact on control systems has not been found sufficient attention in research. Their influence is frequently unnoticed, ignored or not mentioned. This work focuses on the subject of outlier detection and labelling in the context of control system performance analysis. Selected statistical data-driven approaches are analyzed, as they can be easily implemented with limited a priori knowledge. The study consists of a simulation study followed by the analysis of real control data. Different generation mechanisms are simulated, like overlapping Gaussian processes, symmetric and asymmetric, artificially shifted points and fat-tailed distributions. Simulation observations are confronted with industrial control loops datasets. The work concludes with a practical procedure, which should help practitioners in dealing with outliers in control engineering temporal data.