Minsk, Belarus
The method of "moving fractal" using radial basic functions is proposed. From the beginning of monitoring, a fractal is created in the form of a square matrix, the sizes of which are equal to the number of sensors, the fractal moves step by step to receive data at the next time. The matrix parameters calculated at such an offset are compared with the parameters in the previous step. The appearance of a defect is recorded as an abrupt change in the matrix elements. The disadvantage of this method is that it is necessary to model the effect of any type of defect on the parameters of the measuring monitoring system.
condition monitoring, inverse problem, metamodel, fractal, eigenvalue
1. Ni Y. Q., Xia Y., Lin W. e4t al. SHM benchmark for high-rise structures: a reduced-order finite element model and field measurement data. - Smart Structures and Systems. 2012. V. 10. No. 4-5. P. 411-426.
2. Structural damage detection and classification based on machine learning algorithms. / Ed. by T. Burgos, D. Alexander, V. Jaime, et al., 2016. - 10 p.
3. Hoon Sohn., Farrar C. R., Hemez F. M. e t al. A Review of Structural Health Monitoring Literature: 1996-2001. Los Alamos National Laboratory Report, LA-13976-MS, 2004. - Los Alamos (USA): 2004. - 311 c. Dostupno: https://ru.b-ok2.org/book/2551346/640095 (data obrascheniya: 03.04.2020).
4. Benin A. V, Semenov A. S., Semenov S. G. Modelirovanie processa razrusheniya elementov zhelezobetonnyh konstrukciy pod deystviem korrozii armatury. 2010. - Dostupno: https://cyberleninka.ru/article/v/modelirovanie-protsessa-razrusheniya-elementov-zhelezobetonnyh-konstruktsiy-pod-deystviem-korrozii-armatury (data obrascheniya: 09.07.2017).
5. Balayssac J. P., Garnier V. Nondestructive testing and evaluation of civil engineering structures. - Amsterdam: Elsevier, 2017.
6. Klyuev V.V., Sosnin F.R., Kovalev A.V. i dr. Nerazrushayuschiy kontrol' i diagnostika. - M.: Mashinostroenie, 2003. - 656 c.
7. Matzkanin G. A., Yolken H. T. Probability of Detection (POD) for Nondestructive Evaluation (NDE). Nondestructive Testing Information Analysis Center. - Austin, Texas, USA, 2001. - 53 c.
8. Worden K., Farrar C. R., Manson G., Park G. The fundamental axioms of structural health monitoring. - Proc. Royal Soc. A: Math., Phys. & Eng. Sci. 2007. V. 463. No. 2082. P. 1639-1664.
9. Saidov K., Szpytko J. Problems review of the health monitoring of tall type buildings. - J. of KONES. Powertrain and Transport. 2015. V. 22. No. 2. P. 191-204.
10. Vengrinovich V. L. Bayesian Image and Pattern Reconstruction from Incomplete and Noisy Data. - Pattern Recognition and Image Analysis. 2012. V. 22. No. 1. R. 99-107.
11. Yi-zhou Lin, Zhen-hua Nie, Hong-wei Ma. Structural Damage Detection with Automatic Feature-Extraction through Deep Learning. - Computer-Aided Civil and Infrastructure Eng. 2017. V. 32. No.12. R. 1025-1046.
12. Vengrinovich V. L., Klimenko S. V., Rotkov S. I. I dr. Monitoring tehnicheskogo sostoyaniya i ocenka ostatochnogo resursa bol'shih sooruzheniy. - V mire NK. 2015. T. 18. № 3. S. 25-29.
13. Rosenblatt F. The Perceptron: A Probalistic Model For Information Storage And Organization In The Brain. - Psychological Rev. 1958. V. 65. No. 6. R. 386-408.
14. Webb G. T., Vardanega P. J., Middleton C. R. Categories of SHM Deployments: Technologies and Capabilities. - J. Bridge Eng. 2015. V. 20. No. 11. Dostupno: https://ascelibrary.org/doi/10.1061/%28ASCE%29BE.1943-5592.0000735.
15. Vengrinovich V. L., Lykov V. A. Fraktal'nyy analiz bol'shih dannyh na vyhode mnogosensornyh sistem monitoringa. - Nerazrushayuschiy kontrol' i diagnostika. 2015. № 2. S. 5-16.
16. Wenzel H., Veit-Egerer R., Widmann M. The Role of SHM in Civil Life Cycle Engineering. - European Workshop on Structural Health Monitoring. 2012. V. 1. No. 1. R. 10-17.