Automatic detection of anomalies in times series data Big data for smart maintenance

In this thesis techniques in machine learning and data mining are explored for their applicability in a large dataset produced by complex radar systems. This dataset consists of a wide variety of data: logging measurements of many sensors and operations performed by the radar system. All data has one thing in common: they are all time series. The thesis focusses on automatic detection of anomalies in these time series. Anomalies are defined in space and time. Anomalies in time are detected by learning normal behaviour from historic data. Anomalies in space are detected by comparison of the behaviour of similar components. Deviations from the normal behaviour in time and/or space are marked as anomalies. These anomalies can provide feedback for diagnosis, validation and prognosis of the radar system.