Abstract:
This paper presents a way to determine the best
modeling algorithms for working with signals generated by
water pipe leaks. Three methods of parametric modeling are
presented in this paper: auto-regressive modeling AR, moving
average MA modeling and auto-regressive – moving average
ARMA modeling. From these methods, the auto-regressinve
modeling is the best one for analysing signal sequences from
water pipe leaks. A special MATLAB Toolbox was used in
order to work with the signals and the parametric models.
The name of the Toolbox is ARMASA. Several programs were
written in order to work with ARMASA functions and with
the leak signals. The influence of signal length and number
of estimation coefficients, are studied in order to show which
parametric modeling method works best with signals
generated by pipe leaks. The conclusion is that for these type
of signals, the AR autoregressive model is the optimal
solution. With the help of the obtained spectral distribution
values, we can further analyze the signals in order to find with
precision the position of the leaks. After the exact choice of a
parametric modeling algorithm, in ths case the AR model, we
are able to see the benefits of this choice when dealing with
spectral analisys. Signal whitening can be used in order to
improve the quality of the Cross Correlation Function (CCF).
