Automated fault detection method in process data based on cluster analysis

This article presents a method for detecting changes in behavior of data. It is based on cluster analysis, which is a common name for methods that group data in segments called clusters, based on similarities and differences of data itself, without supervision of human observer. The data analyzed by clustering techniques are commonly met in process industry: locally constant process values with a lot of noise and sudden changes to completely different values. The experimental application was developed for evaluation of proposed method and gained results prove its quality for several data patterns. This method can be used for automated fault detection applied to industrial process data when data errors are more complex than simple breaching of data limits or minimum and maximum.