TY - THES

T1 - Machine Learning and KPI Analysis applied to Time-Series Data in Physical Systems

T2 - Comparison and Combination

AU - Haider, Maria

N1 - embargoed until null

PY - 2021

Y1 - 2021

N2 - This thesis examines the combination of deep learning and statistical data analysis for the unsupervised detection of outliers in unlabelled real-time time-series data originating from sensors and actuators. It is investigated if and when the additional application of machine learning is preferable to classical methods, where its strengths and shortcomings lie. A hybrid approach is introduced to enable an exhaustive and precise identification of anomalies. Its components are based on previous work performed by the Chair of Automation at the University of Leoben, which includes the segmentation of different phases in the industrial process and the definition of key point indicators (KPIs) by accessing physical knowledge and experience. The hybrid model contains the calculation of the interquartile range to define outliers, as well as the training and testing of a hyperparameter-optimized deep learning algorithm. The approach was applied to datasets from machinery used in the construction of anchoring piles for the foundations of buildings.

AB - This thesis examines the combination of deep learning and statistical data analysis for the unsupervised detection of outliers in unlabelled real-time time-series data originating from sensors and actuators. It is investigated if and when the additional application of machine learning is preferable to classical methods, where its strengths and shortcomings lie. A hybrid approach is introduced to enable an exhaustive and precise identification of anomalies. Its components are based on previous work performed by the Chair of Automation at the University of Leoben, which includes the segmentation of different phases in the industrial process and the definition of key point indicators (KPIs) by accessing physical knowledge and experience. The hybrid model contains the calculation of the interquartile range to define outliers, as well as the training and testing of a hyperparameter-optimized deep learning algorithm. The approach was applied to datasets from machinery used in the construction of anchoring piles for the foundations of buildings.

KW - Maschinelles Lernen

KW - Ausreisser Detektion

KW - KPI

KW - Statistik

KW - Machine Learning

KW - Deep Learning

KW - Outlier Detection

KW - Statistical Analysis

KW - KPI

M3 - Master's Thesis

ER -