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For many real data, long term observation consists of different processesthat coexist or occur one after the other. Those processes very often exhibitdifferent statistical properties and thus before the further analysis theobserved data should be segmented. This problem one can find in differentapplications and therefore new segmentation techniques have been appeared inthe literature during last years. In this paper we propose a new method of timeseries segmentation, i.e. extraction from the analysed vector of observationshomogeneous parts with similar behaviour. This method is based on the absolutedeviation about the median of the signal and is an extension of the previouslyproposed techniques also based on the simple statistics. In this paper weintroduce the method of structural break point detection which is based on theAdaptive Regression Splines technique, one of the form of regression analysis.Moreover we propose also the statistical test which allows testing hypothesisof behaviour related to different regimes. First, the methodology we apply tothe simulated signals with different distributions in order to show theeffectiveness of the new technique. Next, in the application part we analysethe real data set that represents the vibration signal from a heavy dutycrusher used in a mineral processing plant.

Structural break detection method based on the Adaptive Regression Splines technique