Anomaly Detection for real-time Condition Monitoring.

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The aim of this project is to develop reliable methods of flagging strange behaviour in real world data sets. One such data set might consist of several series of measurements monitoring a system over time. Odd behaviour is often a precursor to something going wrong in a system. Condition monitoring - detecting early warnings of problems in a system for maintenance purposes - is based on this idea.We are interested in two particular types of odd behaviour: anomalies - where behaviour departs from and then returns to the typical; and changepoints - where there is a permanent shift in the typical behaviour shown in a series. Many methods exist to detect anomalies and changepoints, but they can struggle in the face of the difficulties that real world data sets present: such as large size, dependence between series, and changing typical behaviour. The aim of this PhD is to develop methods that work well on data sets that exhibit one or more of these issues. In partnership with Shell.