Anomaly Detection for real-time Condition Monitoring.

这个项目的目的是开发可靠的方法来标记现实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监测系统随时间的测量。奇怪的行为通常是系统出现问题的前兆。状态监测——为维护目的检测系统中问题的早期预警——就是基于这个想法。我们对两种特殊类型的奇怪行为感兴趣：异常——行为偏离典型，然后又回到典型；以及变化点——在一系列的典型行为中有一个永久性的转变。存在许多检测异常和变化点的方法，但它们在面对现实世界数据集存在的困难时可能会挣扎：例如大尺寸，序列之间的依赖性，以及改变典型行为。本博士课程的目的是开发一种方法，可以很好地处理显示其中一个或多个问题的数据集。与壳牌公司合作。

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.