  For many real data, long term observation consists of different processes
that coexist or occur one after the other. Those processes very often exhibit
different statistical properties and thus before the further analysis the
observed data should be segmented. This problem one can find in different
applications and therefore new segmentation techniques have been appeared in
the literature during last years. In this paper we propose a new method of time
series segmentation, i.e. extraction from the analysed vector of observations
homogeneous parts with similar behaviour. This method is based on the absolute
deviation about the median of the signal and is an extension of the previously
proposed techniques also based on the simple statistics. In this paper we
introduce the method of structural break point detection which is based on the
Adaptive Regression Splines technique, one of the form of regression analysis.
Moreover we propose also the statistical test which allows testing hypothesis
of behaviour related to different regimes. First, the methodology we apply to
the simulated signals with different distributions in order to show the
effectiveness of the new technique. Next, in the application part we analyse
the real data set that represents the vibration signal from a heavy duty
crusher used in a mineral processing plant.
