THRESHOLD ASSESSMENT OF THE STATE OF A TECHNICAL OBJECT BASED ON SEGMENTATION AND IDENTIFICATION OF THE CONTROLLED PARAMETER MODEL
Abstract
To fix the jumps in the average value, a detection method based on the segmentation of the signal
under study based on the formation of cumulative sums using the Page-Hinckley criterion is proposed.
The use of the Page–Hinckley likelihood criterion makes it possible to detect abrupt changes
in the average value of the controlled object parameter in real time under noisy conditions. When
using the method, it is assumed that the signal is described by a time series of values of the signal
under study. From this series, it is possible to single out separate successive sections, which can be
considered as some signal models limited in time. The method is based on the use of criterion statistics,
on the basis of which two or three models estimated from different parts of the signal are compared,
which makes it possible to detect abrupt changes in the model parameters. The method assumes
that a piecewise constant signal with additive noise is considered. At arbitrary moments of
time, there are jumps in the average value of this signal. Jumps in the average value of the signal can
be different in sign (fixed on different sides of the time axis) and significantly exceed the original
value in absolute value. The average value of the signal is a constant value close to zero. But a situation
is possible when a repeated jump will be made from a level different from the average value
close to zero, both in the direction of increasing and decreasing the average value of the signal and
changing the signal polarity (the sign of the signal values). A criterion has been chosen that allows
minimizing the delay time in detecting a jump in the average value of the recorded signal with a minimum
of false alarms. In this case, segmentation of the signal under study is used based on the formation
of cumulative sums using the Page-Hinckley criterion. The use of the Page–Hinckley likelihood
criterion makes it possible to detect abrupt changes in the average value of the controlled object
parameter in real time under noisy conditions.
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