Application of Artifi cial Intelligence Technology for Controlling the Onset of Accidents at Common Technical Objects

It is proposed to use the estimates of these noise characteristics as a carrier of diagnostic information, utilizing artificial intelligence technology to monitor the start of the latent period of accidents. It is also shown that it is possible to employ artificial intelligence, using noise as a diagnostic carrier during low-magnitude earthquakes, to monitor the initial changes in the seismic stability of buildings.

Experimental studies have shown that during the operation of the objects under consideration, the occurrence of the latent period of accidents is due to the fact that under actual operating conditions equipment experiences destruction, fatigue cracking, residual stress, fatigue damage, fatigue, wear, and abrasion. As a result, during this period, the estimates of the cross-correlation function between the useful signal and the noise, as well as the noise variance of the vibration signals, are significant values. Furthermore, violations of classical conditions, such as the normality of the distribution law and the stationarity of the signals, have a negligible effect on the resulting estimates. Thanks to these technologies, the estimates of the cross-correlation function between the useful signal and the noise serve as reliable indicators for the onset of accidents using artificial intelligence technology. To ensure the adequacy of the control results, the sampling interval must be determined adaptively in real-time, which is easily achieved by utilizing the frequency properties of the least significant bit of the vibration signal samples.

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