667.64:678.02

management, identification, diagnostics, forecasting, a priori, a posteriori information, modeling.

The issues of control and identification of multidimensional and closed systems of metal structures diagnostics have been considered. The main varieties of identification and modeling of processes for managing the functional diagnostics of metal structures dealing with the improvement of the input information quality due to limited restrictions satisfaction, searching for additional a posteriori information in the process of dynamic changes of the controlled object, forecasting changes in the internal structure of the material have been specified and discussed in detail. The scheme of a priori information in the form of parameterized mappings of inputs and outputs interactions of the diagnostic system has been presented. The results of simulation are associated with the selection of variables measured diagnostic parameters. A methodology for quantitative estimations of posterior inflow information due to entropy values has been developed. Using the marked apertures of modelling and forecasting at each stage of identification, it is possible to obtain a mathematical model adequate to the real situation.

1. Lepikhin, S. V., Gladkovskii, S. V., Granovskii, O. G. The Influence of Low-Temperature Restorative Heat Treatment on the Structure and Mechanical Properties of Boiler Drum Metal after Long-Term Operation (2020) Thermal Engineering, 67 (2), pp. 138–144. DOI: 10.1134/S0040601520020020.
2. Buketov A. Identyfikaciya i modeluvannya tehnologichnyh ob`ektiv ta system. Ternopil: SMP, 2009, “Tajp”, 260 p.
3. Abuelo, A., Brester, J. L., Starken, K., Neuder, L. M. Technical note: Comparative evaluation of 3 methods for the quantification of nonesterified fatty acids in bovine plasma sampled prepartum (2020), Journal of Dairy Science, 103 (3), pp. 2711–2717. DOI: 10.3168/jds.2019-17527.
4. Jin, H., Kim, J. H. Evaluation of Feature Robustness Against Technical Parameters in CT Radiomics: Verification of Phantom Study with Patient Dataset (2020) Journal of Signal Processing Systems, 92 (3), pp. 277–287. Cited 1 time. DOI: 10.1007/s11265-019-01496-z.
5. Noguerol, T. M., Barousse, R., Amrhein, T. J., Royuela-Del-val, J., Montesinos, P., Luna, A. Optimizing diffusion-tensor imaging acquisition for spinal cord assessment: Physical basis and technical adjustments (2020) Radiographics, 40 (2), pp. 403–427. DOI: 10.1148/rg.2020190058.
6. Levin, V. M., Yahya, A. A. Adaptive management of technical condition of power transformers (2020) International Journal of Electrical and Computer Engineering, 10 (4), pp. 3862–3868. DOI: 10.11591/ ijece.v10i4. Р. 3862–3868.
7. Eltyshev, D. K., Kostygov, A. M. Intelligent Diagnostic Control and Management of the Condition of Electrotechnical Equipment (2019), Russian Electrical Engineering, 90 (11), pp. 741–746. DOI: 10.3103/ S1068371219110038
8. Abejirinde, I.-O. O., De Brouwere, V., van Roosmalen, J., van der Heiden, M., Apentibadek, N., Bardajн, A., Zweekhorst, M. Viability of diagnostic decision support for antenatal care in rural settings: Findings from the Bliss4Midwives Intervention in Northern Ghana (2019), Journal of Global Health,
   9 (1), art. no. 010420, Cited 1 time. DOI: 10.7189/jogh.09.010420.
9. Kanaev, A. K., Saharova, M. A., Beneta, E. V. Neural network model for the solution of tasks of technical diagnostics of the transport telecommunication network (2016), Proceedings of the 19th International Conference on Soft Computing and Measurements, SCM 2016, art., no. 7519728, pp. 203–205. DOI: 10.1109/SCM.2016.7519728.
10. Protalinsky O., Khanova, A., Shcherbatov, I. Simulation of power assets management process (2019) Studies in Systems, Decision and Control, 199, pp. 488–501. Cited 3 times. DOI: 10.1007/978-3-030-12072-6_40.
11. Zozulia A., Lytvynenko Ia., Lutsyk N., Lupenko S., Yasniy O. Method of vector rhythmcardiosignal automatic generation in computer-based systems of heart rhythm analysis. Scientific Journal of TNTU. Tern.: TNTU, 2020. Vol. 97. No. 1. P. 122–132.
12. Lytvynenko V., Lur`e I., Boskin O. Okipnyi I. Automation of acoustic-emission diagnostic systems control processes. Scientific Journal of TNTU. Tern.: TNTU, 2020. Vol. 97. No. 1. P. 88–96.

1. Lepikhin, S. V., Gladkovskii, S. V., Granovskii, O. G. The Influence of Low-Temperature Restorative Heat Treatment on the Structure and Mechanical Properties of Boiler Drum Metal after Long-Term Operation (2020) Thermal Engineering, 67 (2), pp. 138–144. DOI: 10.1134/S0040601520020020.
2. Buketov A. Identyfikaciya i modeluvannya tehnologichnyh ob`ektiv ta system. Ternopil: SMP, 2009, “Tajp”, 260 p.
3. Abuelo, A., Brester, J. L., Starken, K., Neuder, L. M. Technical note: Comparative evaluation of 3 methods for the quantification of nonesterified fatty acids in bovine plasma sampled prepartum (2020), Journal of Dairy Science, 103 (3), pp. 2711–2717. DOI: 10.3168/jds.2019-17527.
4. Jin, H., Kim, J. H. Evaluation of Feature Robustness Against Technical Parameters in CT Radiomics: Verification of Phantom Study with Patient Dataset (2020) Journal of Signal Processing Systems, 92 (3), pp. 277–287. Cited 1 time. DOI: 10.1007/s11265-019-01496-z.
5. Noguerol, T. M., Barousse, R., Amrhein, T. J., Royuela-Del-val, J., Montesinos, P., Luna, A. Optimizing diffusion-tensor imaging acquisition for spinal cord assessment: Physical basis and technical adjustments (2020) Radiographics, 40 (2), pp. 403–427. DOI: 10.1148/rg.2020190058.
6. Levin, V. M., Yahya, A. A. Adaptive management of technical condition of power transformers (2020) International Journal of Electrical and Computer Engineering, 10 (4), pp. 3862–3868. DOI: 10.11591/ ijece.v10i4. Р. 3862–3868.
7. Eltyshev, D. K., Kostygov, A. M. Intelligent Diagnostic Control and Management of the Condition of Electrotechnical Equipment (2019), Russian Electrical Engineering, 90 (11), pp. 741–746. DOI: 10.3103/ S1068371219110038
8. Abejirinde, I.-O. O., De Brouwere, V., van Roosmalen, J., van der Heiden, M., Apentibadek, N., Bardajн, A., Zweekhorst, M. Viability of diagnostic decision support for antenatal care in rural settings: Findings from the Bliss4Midwives Intervention in Northern Ghana (2019), Journal of Global Health,
   9 (1), art. no. 010420, Cited 1 time. DOI: 10.7189/jogh.09.010420.
9. Kanaev, A. K., Saharova, M. A., Beneta, E. V. Neural network model for the solution of tasks of technical diagnostics of the transport telecommunication network (2016), Proceedings of the 19th International Conference on Soft Computing and Measurements, SCM 2016, art., no. 7519728, pp. 203–205. DOI: 10.1109/SCM.2016.7519728.
10. Protalinsky O., Khanova, A., Shcherbatov, I. Simulation of power assets management process (2019) Studies in Systems, Decision and Control, 199, pp. 488–501. Cited 3 times. DOI: 10.1007/978-3-030-12072-6_40.
11. Zozulia A., Lytvynenko Ia., Lutsyk N., Lupenko S., Yasniy O. Method of vector rhythmcardiosignal automatic generation in computer-based systems of heart rhythm analysis. Scientific Journal of TNTU. Tern.: TNTU, 2020. Vol. 97. No. 1. P. 122–132.
12. Lytvynenko V., Lur`e I., Boskin O. Okipnyi I. Automation of acoustic-emission diagnostic systems control processes. Scientific Journal of TNTU. Tern.: TNTU, 2020. Vol. 97. No. 1. P. 88–96.