Technology of Adaptive Control of the Beginning of the Latent Period of Accidents of Objects

Nowadays the number of unexpected accidents depends to a great extent on the qualification of the personnel, who on the basis of the accumulated experience and the information received from the control systems intuitively determine the current technical condition of the object and the beginning of possible accidents. Sometimes their decision is too late and accidents occur. Usually, accidents occur as a result of occurrence and development of various malfunctions and at this time objects go into the latent period of emergency state, there noise emerges, correlated with the signal g(t) under control. Here, depending on the nature of the malfunction the dynamics of technological processes changes, under the influence of which, the spectra of useful signals X(t) and noise ε(t), g(t) = X(t) + ε(t) also change. When using the traditional technology, the noise is filtered. This introduces an additional error in the result of signal analysis, leading to a loss of important diagnostic information. To solve the problems of control of the beginning of the latent period of accidents it is proposed to use the estimate of the cross-correlation function between the useful signal and the noise as an informative attribute, using the technology of adaptive sampling of the analyzed signals g(t). To ensure the adequacy of the results of control by combining the proposed and traditional technologies information is formed about the beginning of the latent period of accidents, which allows the master to determine the time of the beginning of the latent period of accidents with sufficient reliability.

1. Aliev T. A. Noise control of the Beginning and Development Dynamics of Accidents, New York, Springer, 2019, 201 p., doi: 10.1007/978-3-030-12512-7.

2. Aliev T. A., Guluyev G. A., Pashayev F. H. et al. Noise monitoring technology for objects in transition to the emergency state. Mechanical Systems and Signal Processing, 2012, vol. 27, pp. 755—762, doi: 10.1016/j.ymssp.2011.09.005.

3. Aliev T. A., Alizada T. A., Rzayeva N. E. et al. Noise technologies and systems for monitoring the beginning of the latent period of accidents on fixed platforms. Mechanical Systems and Signal Processing, 2017, vol. 87, pp. 111—123, doi: 10.1016/j.ymssp.2016.10.014.

4. Aliev T. A., Rzayev A. H., Guluyev G. A. et al. Robust technology and system for management of sucker rod pumping units in oil wells. Mechanical Systems and Signal Processing, 2018, vol. 99, pp. 47—56, doi: 10.1016/j.ymssp.2017.06.010.

5. Aliev T. A., Guluyev G. A., Rzayev A. H., Pashayev F. H. Technologies and system for signaling the beginning of accidents on drilling rigs based on the wattmeter charts of their electric motors, Oil and Gas Wells — Recent Advances in Drilling and Completion Technologies, pp. 87—103, doi: 10.5772/intechopen.111666.

6. Aliev T. A., Musaeva N. F., Babayev T. A. et al. Technologies and intelligent systems for adaptive vibration control in rail transport. Transport Problems: An International Scientific Journal, 2022, vol. 17, iss. 3, pp. 31—38, doi: 10.20858/tp.2022.17.3.03.

7. Aliev T. A., Rzayeva N. E., Sattarova U. E. Robust correlation technology for online monitoring of changes in the state of the heart by means of laptops and smartphones, Biomedical Signal Processing and Control, 2017, vol. 31, pp. 44—51, doi: 10.1016/j.bspc.2016.06.015.

8. Aliev T. A., Alizada T. A., Rzayeva N. E. Noise control of heart by means of a smartphone, Saarbr@cken, Lambert Academic Publishing, 2012, 156 p.

9. Aliev T. A., Guluyev G. A., Pashayev F. H. et al. Intelligent seismic-acoustic system for identifying the location of the areas of an expected earthquake. Journal of Geoscience and Environment Protection, 2016, vol. 4, no. 4, pp. 147—162, doi: 10.4236/gep.2016.44018.

10. Aliev T. A., Abbasov A. M., Guluyev G. A. et al. System of robust noise monitoring of anomalous seismic processes, Soil Dynamics and Earthquake Engineering, 2013, vol. 53, pp. 11—25, doi: 10.1016/j.soildyn.2012.12.013.

11. Aliev T. A., Abbasov A. M., Aliev E. R. et al. Digital technology and systems for generating and analyzing information from deep strata of the earth for the purpose of interference monitoring of the technical state of major structures, Automatic Control and Computer Sciences, 2007, vol. 41, no. 2, pp. 59—67, doi: 10.3103/ S0146411607020010.

12. Aliev T. A. Intelligent seismic-acoustic system for identifying the area of the focus of an expected earthquake, Taher Zouaghi (ed) Earthquakes: Tectonics, Hazard and Risk Mitigation, London, Intech, 2017, pp. 293—315, doi: 10.5772/65403.

13. Aliev T. A., Abbasov A. M., Mamedova G. G. et al. Technologies for noise monitoring of abnormal seismic processe, Seismic Instruments, 2013, vol. 49, no. 1, pp. 64—80, doi: 10.3103/S0747923913010015.

14. Aliev T. A., Aliev E. R. Multichannel telemetric system for seismo-acoustic signal interference monitoring of earthquakes, Automatic Control and Computer Sciences, 2008, vol. 42, no. 4, pp. 223—228, doi: 10.3103/S0146411608040093.

15. Aliev T. A, Mammadova A. I. Principle of Construction of Analog-to-Digital Converters with Adaptive Determination of Sampling Interval of Analyzed Signals Mekhatronika, Avtomatizatsiya, Upravlenie, 2024, vol.25, no.8, pp. 401—406, doi: 10.17587/mau.25.401-406.

16. Fasel T. R., Todd M. D..Chaotic insonification for health monitoring of an adhesively bonded composite stiffened panel, Mechanical Systems and Signal Processing, 2010, vol. 24, no. 5, pp. 1420—1430.

17. Tang H., Liao Y. H., Cao J. Y., Xie H. Fault diagnosis approach based on Volterra models, Mechanical Systems and Signal Processing, 2010, vol. 24, no. 4, pp. 1099—1113, doi: 10.1016/j.ymssp.2009.09.001.

18. Dong G., Chen P. The vibration characteristics of drillstring with positive displacement motor in compound drilling. Part1: Dynamical modelling and monitoring validation, International Journal of Hydrogen Energy, 2018, vol. 43, no. 5, pp. 2890—2902, doi: 10.1016/j.ijhydene.2017.12.161.

19. Ghasemloonia A., Rideout D. G., Butt S. D. A review of drillstring vibration modeling and suppression methods, Journal of Petroleum Science and Engineering, 2015, vol. 131, pp. 150—164, doi: 10.1016/j.petrol.2015.04.030.

20. Rakus-Andersson E. Fuzzy and Rough Techniques in Medical Diagnosis and Medication, Berlin, Heidelberg, Springer, 2007, 212 p., doi: 10.1007/978-3-540-49708-0.

21. Hoover A., Singh A., Fishel-Brown S. et al. Real-time detection of workload changes using heart rate variability, Biomedical Signal Processing and Control, 2012, vol. 7, no. 4, pp. 333—341, doi: 10.1016/j.bspc.2011.07.004.

22. García C. A., Otero A., Vila X. et al. A new algorithm for wavelet-based heart rate variability analysis, Biomedical Signal Processing and Control, 2013, vol. 8, no. 6, pp. 542—550, doi: 10.1016/j.bspc.2013.05.006.

23. Kanamori H., Brodsky E. The physics of earthquakes, Reports on Progress in Physics, 2004, vol. 67, no. 8, pp. 1429—1496, doi: 10.1088/0034-4885/67/8/R03.

24. Tothong P., Cornell C. A. An empirical ground-motion attenuation relation for inelastic spectral displacement, Bulletin of the Seismological Society of America, 2006, vol. 96, no. 6, pp. 2146—2164, doi: 10.1785/0120060018.

25. Ghahari S. F., Jahankhah H., Ghannad M. A. Study on elastic response of structures to near-fault ground motions through record decomposition, Soil Dynamics and Earthquake Engineering, 2010, vol. 30, no. 7, pp. 536—546, doi: 10.1016/j.soildyn.2010.01.009.

26. Boore D. M., Bommer J. J. Processing of strong-motion accelerograms: needs, options and consequences, Soil Dynamics and Earthquake Engineering, 2005, vol. 25, no. 2, pp. 93—115, doi: 10.1016/j.soildyn.2004.10.007.

27. Galiana-Merino J. J., Parolai S., Rosa-Herranz J. Seismic wave characterization using complex trace analysis in the stationary wavelet packet domain, Soil Dynamics and Earthquake Engineering, 2011, vol. 31, no. 11, pp. 1565—1578, doi: 10.1016/j.soildyn.2011.06.009.

28. Yee E., Stewart J. P., Schoenberg F. P. Characterization and utilization of noisy displacement signals from simple shear device using linear and kernel regression methods, Soil Dynamics and Earthquake Engineering, 2011, vol. 31, no. 1, pp. 25—32, doi: 10.1016/j.soildyn.2010.07.011.