Setting up the Attribute Space of Periodic Time Dependencies for Dynamic Object Diagnostic Systems (Taking the Eye Retina as an Example)

Abstract

The technology of testing dynamically and structurally similar aircraft models for flutter in subsonic wind tunnels using information and The article addresses techniques for setting up the attribute space of informative features of periodic signals recorded at the output of a dynamic object with an unknown structure in response to rectangular testing signals of different frequencies applied to the object input. The attribute space is used in developing expert systems for diagnosing the current state of an operating dynamic object. With a great variety of possible developing faults, the simplest practical techniques involving the use of characteristic points of change in the observed time dependencies yield a limited number of features with large mutual intersection domains. To expand the attribute space, it is proposed to use the expansion of input and output signals into a Fourier series for setting up a base of additional features. The proposed features characterize, depending on the testing conditions, the object’s transferring properties in the frequency domain from changes in its amplitude and phase characteristics. The test pulse frequency and duration serve as such conditions. For the convenience of comparing the object’s frequency responses variation pattern, two special procedures are used. The first procedure allows the observed time dependencies to be reduced to a single pseudo frequency of the test signals. The second procedure uses specially formed windows for subjecting individual fragments of the observed time dependencies to a spectral analysis. It is shown that, depending on the type of the frequency responses being analyzed, the techniques for their polynomial approximation, as well as integral estimates of frequency response individual domains can be useful. The polynomial approximation makes it possible to use the coefficients of the approximating polynomials as additional features, and the integration of individual characteristic domains of the frequency responses makes it possible to introduce dimensionless relative indicators that characterize the degree of change in the frequency responses depending on the experimental conditions. The considered techniques open the possibility to select additional features that can help distinguish both separate groups of faults and individual faults in operating objects. The study results are illustrated by the examples of analyzing the changes in electroretinograms that record changes in the eye retina biopotential in response to light flashes of different frequencies.