Analysis of Discrete Wavelet Spectra of Broand Signals

Abstract

The article is devoted to the development of a mathematical model for autoregressive and autocoherent analysis of discrete wavelet spectra with approbation on known experimental data. The model is used to detect anomalies or emergency states of the object after wavelet filtering of noise and zero crossing. The proposed method for processing noisy signals, combining noise filtering and Mahalanobis proximity analysis, shows better machine learning results than neural networks and other methods for detecting classification and recognition anomalies. The wavelet spectra were processed using autoregressive and autocoherent analyses. The possibility of using these functions as classification features for identifying possible anomalous states of devices is shown. Before forming the wavelet spectra, the procedure of direct with filtering and inverse transformation of the already filtered signal is carried out. As a result, it seems possible to identify the characteristic features of the signal -anomalies or emergency conditions. The main factor in the classification of device status signals is the moment when changes in the signal spectrum begin to develop, therefore, all analysis, both in the time and frequency domain, is tied either to the number of samples by the time the signal was received, and in some cases even to the extended date format -month number.