| 摘要 |
<p>A method of system monitoring or, more particularly, novelty detection (in engineering, health, and finance fields), is based on extreme value theory in particular a points-over-threshold POT method which is applicable to multimodal multivariate data. Multimodal multivariate data points collected by continuously monitoring a system are transformed into probability space by obtaining their probability density function (pdf) values from a statistical model of normality, such as a pdf fitted to a training data set of normal data. Extremal data is defined as that whose pdf value is below a predetermined threshold and a new analytic function, in particular the Generalised Pareto Distribution (GPD) is fitted to that extremal data only. The fitted GPD can be compared to a GPD fitted to the extremal datapoints of the training data set of normal data to determine if the monitored system is in a normal state. Alternatively a threshold can be set by calculating an extreme value distribution of the GPD fitted to the extremal data of the training data set and setting as the threshold the pdf value which separates a desired proportion, eg 0.99 of the probability mass from the remainder. If the minimum pdf value of a set of data points collected from the system is below the threshold, the system may be abnormal.</p> |
