Anomaly Detection for real-time Condition Monitoring.

该项目的目的是开发可靠的方法，标记真实的世界数据集中的奇怪行为。一个这样的数据集可能包括几个系列的测量监测系统随着时间的推移。奇怪的行为往往是系统出错的前兆。状态监测-检测系统中的问题的早期预警，用于维护目的-是基于这一想法。我们感兴趣的是两种特殊类型的奇怪的行为：异常-行为偏离，然后返回到典型的;和变点-在一个系列中显示的典型行为中有一个永久的转变。存在许多方法来检测异常和变点，但它们在面对真实的世界数据集所呈现的困难时可能会遇到困难：例如大尺寸，系列之间的依赖性以及不断变化的典型行为。这个博士学位的目的是开发出在表现出一个或多个这些问题的数据集上工作良好的方法。与壳牌合作。

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.