Istrazivanja i projektovanja za privreduJournal of Applied Engineering Science


DOI: 10.5937/jaes0-27886 
This is an open access article distributed under the CC BY 4.0
Creative Commons License

Volume 18 article 725 pages: 537 - 554

Ivan V. Devyatov
Lomonosov Moscow State University, Faculty of Economics, Scientific laboratory of economic research of the military-industrial complex development , Moscow, Russia

Sergei A. Iurgenson
Lomonosov Moscow State University, Faculty of Economics, Scientific laboratory of economic research of the military-industrial complex development , Moscow, Russia

Ivan A. Zharenov
Lomonosov Moscow State University, Faculty of Economics, Scientific laboratory of economic research of the military-industrial complex development , Moscow, Russia

Andrei A. Trutnenko
Lomonosov Moscow State University, Faculty of Economics, Scientific laboratory of economic research of the military-industrial complex development , Moscow, Russia

Dmitrii V. Tuev*
Lomonosov Moscow State University, Faculty of Economics, Scientific laboratory of economic research of the military-industrial complex development , Moscow, Russia

This article defines the basic set of means and methods of the structural health monitoring of metal structures and structures from polymer composite materials (PCMs) used in the construction and aviation industry. The analysis of means and methods of strain-stress state control of a structure during operation (detection of defects and crack growth) and their comparison is provided.

View article

This research is carried out with the financial support of the Ministry of Science and Higher Education of the Russian Federation under the grant agreement No. 05.607.21.0329 (internal number of the Agreement is 05.607.21.0329) of December 18, 2019 (unique number is RFMEFI60719X0329). The applied research is conducted on the topic "Development of basic design solutions for the structural health monitoring system of structures and complex engineering products in order to ensure the man-made safety, sustainable functioning of critical infrastructure of the Russian Federation and the development of domestic industry sectors".

1. Aghayan, G. M., Grigoryan, A. A., Shikin, E. V., Shikina, G. E. (2014). On the stages of the crisis process and crises that can be prevented. Moscow University Bulletin. Series 21. Management (State and Society), 4, 22-44. ob-etapah-krizisnogo-protsessa-i-krizisah-kotorye- mozhno-predotvratit

2. Maksakovsky, V. P. (2008). Geographical Picture of the World. In 2 books. Book I. General Characteristics of the World. 4th ed. Drofa, Moscow.

3. Naumova, T. V. (2009). Concept of global problems in the I.T. Frolov’s philosophy. Vestnik of the Orenburg State University, 7(101), 81-87. http://vestnik.

4. Silva, M., Santos, A., Santos, R., Figueiredo, E., Sales, C., Costa, J. C. W. A. (2017). Agglomerative concentric hypersphere clustering applied to structural damage detection. Mechanical Systems and Signal Processing, 92, 196-212. doi: 10.1016/j.ymssp. 2017.01.024

5. Barontini, A., Masciotta, M. G., Ramos, L. F., Amado- Mendes, P., Lourenço, P. B. (2017). An overview on nature-inspired optimization algorithms for structural health monitoring of historical buildings. Procedia Engineering, 199, 3321-3325. doi: 10.1016/J. PROENG.2017.09.439

6. Finotti, R. P., Barbosa, F. S., Cury, A. A., Gentile, C. (2017). A novel natural frequency-based technique to detect structural changes using computational intelligence. Procedia Engineering, 199, 3314-3319. doi: 10.1016/J.PROENG.2017.09.438

7. Serov, A. (2017). Cognitive sensor technology for structural health monitoring. Procedia Structural Integrity, 5, 1160-1167. doi: 10.1016/J.PROSTR. 2017.07.027

8. Yi, T. H., Huang, H. B., Li, H. N. (2017). Development of sensor validation methodologies for structural health monitoring: A comprehensive review. Measurement, 109, 200-214. doi: 10.1016/J.MEASUREMENT. 2017.05.064

9. Papa, U., Russo, S., Lamboglia, A., Del Core, G., Iannuzzo, G. (2017). Health structure monitoring for the design of an innovative UAS fixed wing through inverse finite element method (iFEM). Aerospace Science and Technology, 69, 439-448. doi: 10.1016/J.AST.2017.07.005

10. Dong, C. Z., Ye, X. W., Jin, T. (2018). Identification of structural dynamic characteristics based on machine vision technology. Measurement: Journal of the International Measurement Confederation, 126, 405- 416. doi: 10.1016/J.MEASUREMENT.2017.09.043

11. Abdaoui, A., El Fouly, T. M., Ahmed, M. H. (2017). Impact of time synchronization error on the modeshape identification and damage detection/localization in WSNs for structural health monitoring. Journal of Network and Computer Applications, 83, 181-189. doi: 10.1016/j.jnca.2017.01.004

12. Krichen, D., Abdallah, W., Boudriga, N. (2017). On the design of an embedded wireless sensor network for aircraft vibration monitoring using effcient game theoretic based MAC protocol. Ad Hoc Networks, 61, 1-15. doi: 10.1016/j.adhoc.2017.03.004

13. Lourens, E., Fallais, D. J. M. (2017). On the use of equivalent forces for structural health monitoring based on joint input-state estimation algorithms. Procedia Engineering, 199, 2140-2145. doi: 10.1016/J. PROENG.2017.09.152

14. Zhang, Z., Luo, Y. (2017). Restoring method for missing data of spatial structural stress monitoring based on correlation. Mechanical Systems and Signal Processing, 91, 266-277. doi: 10.1016/J.YMSSP. 2017.01.018

15. Nagarajaiah, S. (2017). Sparse and low-rank methods in structural system identification and monitoring. Procedia Engineering, 199, 62-69. doi: 10.1016/J. PROENG.2017.09.153

16. Loutas, T. H., Bourikas, A. (2017). Strain sensors optimal placement for vibration-based structural health monitoring. The effect of damage on the initially optimal configuration. Journal of Sound and Vibration, 410, 217-230. doi: 10.1016/J.JSV.2017.08.022

17. Roth, W., Giurgiutiu, V. (2017). Structural health monitoring of an adhesive disbond through electromechanical impedance spectroscopy. International Journal of Adhesion and Adhesives, 73, 109-117. doi: 10.1016/J.IJADHADH.2016.11.008

18. Di Lorenzo, E., Manzato, S., Peeters, B., Marulo, F., Desmet, W. (2017). Structural health monitoring strategies based on the estimation of modal parameter. Procedia Engineering, 199, 3182-3187.

19. Cardoso, R., Cury, A., Barbosa, F. (2017). A robust methodology for modal parameters estimation applied to SHM. Mechanical Systems and Signal Processing, 95, 24-41.

20. Chen, J. G., Buyukozturk, O. (2017). A symmetry measure for damage detection with mode shapes. Journal of Sound and Vibration, 408, 123-137. doi: 10.1016/j.jsv.2017.07.022

21. Furtmuller, T., Adam, C. (2017). Compensation of temperature effects in long-term monitoring of a highway bridge located in the Austrian Alps. Procedia Engineering, 199, 2078-2083. doi: 10.1016/j.proeng. 2017.09.477

22. Sarda-Espinosa, A., Subbiah, S., Bartz-Beielstein, T. (2017). Conditional inference trees for knowledge extraction from motor health condition data. Applications of Artificial Intelligence, 62, 26-37. doi: 10.1016/j.engappai.2017.03.008

23. Li, D. (2016). Discussion of model reduction and reservation. Procedia Engineering, 188, 354-362. doi: 10.1016/j.proeng.2017.04.495

24. Smith, C. B., Hernandez, E. M. (2017). Exploiting spatial sparsity in vibration-based damage detection. Procedia Engineering, 199, 1925-1930. doi: 10.1016/J.PROENG.2017.09.284

25. Turnbull, H., Omenzetter, P. (2017). Fuzzy fi nite element model updating of a laboratory wind turbine blade for structural modification detection. Procedia Engineering, 199, 2274-2281. doi: 10.1016/j.proeng. 2017.09.258

26. Huang, Y., Beck, J.L., Li, H. (2017). Hierarchical sparse Bayesian learning for structural damage detection: Theory, computation and application. Safety, 64, 37-53. doi: 10.1016/J.STRUSAFE.2016.09.001

27. Sepe, V., Valente, C., Zuccarino, L., Siano, R., Iezzi, F. (2017). Identification of frame models under unmeasured base motion: experimental validation. Procedia Engineering, 199, 1008-1014.

28. Kim, W., Yi, J. H., Kim, J. T., Park, J. H. (2017). Vibration- Based Structural Health Assessment of a Wind Turbine Tower Using a Wind Turbine Model. Procedia Engineering, 188, 333-339. doi: 10.1016/j. proeng.2017.04.492

29. Sagar, R. V. (2017). Acoustic emission characteristics of reinforced concrete beams with varying percentage of tension steel reinforcement under flexural loading. Studies in Construction Materials, 6, 162- 176. doi: 10.1016/j.cscm.2017.01.002

30. Sternini, S., Quattrocchi, A., Montanini, R., Pau, A., Di Scalea, F. L. (2017). A match coefficient approach for damage imaging in structural components by ultrasonic synthetic aperture focus. Procedia Engineering, 199, 1544-1549. doi: 10.1016/j. proeng.2017.09.503

31. Park, J., Lee, M. J., Jeon, J. Y., Jung, H. K., Park, G., Kang, T., Han, S. W. (2017). Compressive sensing approaches for condition monitoring and laser-scanning based damage visualization. Procedia Engineering, 188, 80-88. doi: 10.1016/J.PROENG. 2017.04.460

32. Kullaa, J. (2017). Development of virtual sensors to increase the sensitivity to damage. Procedia Engineering, 199, 1937-1942. doi: 10.1016/j.proeng. 2017.09.290

33. Lize, E., Rebillat, M., Mechbal, N., Bolzmacher, C. (2017). Estimation of the temperature field on a composite fan cowl using the static capacity of surface- mounted piezoceramic transducers. IFAC-PapersOnLine, 50(1), 11689-11694. doi: 10.1016/J. IFACOL.2017.08.1685

34. Berdnik, M. M., Vishnevskaya, N. S. (2014). Building constructions: Training manual. 2nd ed. USTU, Ukhta.

35. Gabrusenko, V. V. (2012). Effect of factory technology defects on strength, rigidity and crack resistance of concrete structures. Novosibirsk State University of Architecture and Civil Engineering, Novosibirsk.

36. Ignatovich, S., Karuskevich, M., Bouraou, N., Krasnopolskiy, V. (2011). Prospects of the application of the onboard automated fatigue life exhaustion monitoring systems for aircraft structures. Vestnik of TNTU, 2, 136-143. http://elartu. 2011_Special1-Ignatovich_S_Karuskevich_ M_Bouraou_N-Prospects_of_the_application_ of_the__136.pdf

37. Volkswagen AG. (n.d.). Virtual technologies. https:// technologies.html

38. Glass. (n.d.).

39. Medvedskiy, A. L., Martirosov, M. I., Khomchenko, A. V., Dedova, D. V. (2020). Assessment of the strength of a composite package with internal defects according to various failures criteria under the influence of unsteady load. Periodico Tche Quimica, 17(35), 1218-1230. br/arquivos_jornal/2020/35/100_MEDVEDSKIY_ pgs_1218_1230.pdf

40. Sadritdinov, A. R., Khusnullin, A. G., Psyanchin, A. A., Zakharova, E. M., Zakharov, V. P. (2020). Physical and mechanical and thermophysical properties of polymer composites based on recycled polypropylene fi lled with rice husk. Periodico Tche Quimica, 17(35), 703-712. br/arquivos_jornal/2020/35/60_SADRITDINOV_ pgs_703_712.pdf

41. Government of the Russian Federation. (2003). On the unifi ed state system for the prevention and elimination of emergency situations. https://www.mchs.

42. Federal Agency on Technical Regulating and Metrology. (2005). Safety in Emergencies. Structured System for Monitoring and Control of Building/Construction Engineering Equipment. General Requirements. smis/gost-r-22-1-12-2005