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Technologies for monitoring the technical condition of transport infrastructure objects based on the coefficient of correlation between critical values of noise and useful signals

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Transport infrastructure objects are exposed to a large number of loads, which cause the formation of displacements, bends, wear, cracks, breakdowns, corrosion, and other defects. It is shown that at the moment of initiation of malfunctions in objects, the noise of the noisy signals coming from the corresponding sensor takes critical values that correlate with useful signals. Therefore, algorithms are developed for calculating the probability of random noise accepting critical values, a coefficient of correlation between the critical values of the noise and the useful component, and a relay cross-correlation function. Technologies for monitoring the technical condition of transport infrastructure objects are proposed based on the estimates of the developed noise characteristics. Computational experiments are conducted, and the reliability of the developed algorithms and technologies is confirmed.
Czasopismo
Rocznik
Strony
213--224
Opis fizyczny
Bibliogr. 16 poz.
Twórcy
autor
  • Institute of Control Systems of the Azerbaijan National Academy of Sciences; 68, B.Vahabzade, Baku AZ1141, Azerbaijan
  • Azerbaijan University of Architecture and Construction; 11, A. Sultanova, Baku AZ1073, Azerbaijan
Bibliografia
  • 1. Deng, T. Impacts of transport infrastructure on productivity and economic growth: recent advances and research challenges. Transport Reviews: A Transnational Trans Disciplinar Journal. 2013. Vol. 33. No. 6. P. 686-699. DOI: https://doi.org/10.1080/01441647.2013.851745.
  • 2. Prus, P. & Sikora, M. The impact of transport infrastructure on the sustainable development of the region – case study. Agriculture. 2021. Vol. 11(4). P. 279. DOI: https://doi.org/10.3390/agriculture11040279.
  • 3. Caldera, S. & Mostafa, S. & Desha, C. & Mohamed, S. Exploring the role of digital infrastructure asset management tools for resilient linear infrastructure outcomes in cities and towns: a systematic literature review. Sustainability. 2021. Vol. 13. No. 21. P. 11965. DOI: https://doi.org/10.3390/su132111965.
  • 4. Gura, D.A. & Kiryunikova, N.M. & Lesovaya, E.D. & Khusht, & N.I. Pavlukova A.P. & Podtelkov V.V. Geodetic monitoring system to ensure safe operation of infrastructure facilities. In: 2020 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon). 2020. P. 1-6. DOI: https://doi.org/10.1109/FarEastCon50210.2020.9271604.
  • 5. Gura, D. & Markovskii, I. & Khusht, N. & Rak, I. & Pshidatok, S. A Complex for monitoring transport infrastructure facilities based on video surveillance cameras and laser scanners. Transportation Research Procedia. 2021. Vol. 54. P. 775-782. DOI: https://doi.org/10.1016/j.trpro.2021.02.130.
  • 6. McGetrick, P.J. & Hester, D. & Taylor, S.E. Implementation of a drive-by monitoring system for transport infrastructure utilising smartphone technology and GNSS. Journal of Civil Structural Health Monitoring. 2017. Vol. 7. P. 175-189. Available at: https://link.springer.com/article/10.1007/s13349-017-0218-7.
  • 7. Jia, H. & Jia, K. & Sun, C. & et al. Preliminary numerical study on seismic response of ordinary long-span suspension bridges crossing active faults. Advances in Bridge Engineering. 2021. Vol. 2. No 16. P.1-11. DOI: https://doi.org/10.1186/s43251-021-00035-w.
  • 8. Mao, J. & Wang, H. & Xu, Y. & et al. Deformation monitoring and analysis of a long-span cablestayed bridge during strong typhoons. Advances in Bridge Engineering. 2020. Vol. 1. No. 8. P. 1-19. DOI: https://doi.org/10.1186/s43251-020-00008-5.
  • 9. Yu, E. & Wei, H. & Han, Y. & et al. Application of time series prediction techniques for coastal bridge engineering. Advances in Bridge Engineering. 2021. Vol. 2. No. 6. P. 1-18. DOI: https://doi.org/10.1186/s43251-020-00025-4.
  • 10. OBrien, E.J. & Leahy, C. & Enright, B. & Caprani, C.C. Validation of scenario modelling for bridge loading. The Baltic Journal of Road and Bridge Engineering. 2016. Vol. 11. No. 3. P. 233-241. DOI: https://doi.org/10.3846/bjrbe.2016.27.
  • 11. Arafa, A. & Ahmed, N. & Farghaly, A.S. & Chaallal, O. & Benmokrane, B. Exploratory study on incorporating glass FRP reinforcement to control damage in steel-reinforced concrete bridge pierwalls. Journal of Bridge Engineering. 2021. Vol. 26. No. 2. DOI: https://doi.org/10.1061/(ASCE)BE.1943-5592.0001648.
  • 12. Yang, Y.B. & Yang, Judy P. State-of-the-art review on modal identification and damage detection of bridges by moving test vehicles. International Journal of Structural Stability and Dynamics. 2018. Vol. 18. No. 2. DOI: https://doi.org/10.1142/S0219455418500256.
  • 13. Yang, Y.-B. & Lin, C.W. & Yau, J.D. Extracting bridge frequencies from the dynamic response of a passing vehicle. Journal of Sound and Vibration. 2004. Vol. 272. Nos. 3-5. P. 471-493. DOI: https://doi.org/10.1016/S0022-460X(03)00378-X.
  • 14. Guo, W.G. & Jin, J.J. & Hu, S.J. Profile monitoring and fault diagnosis via sensor fusionfor ultrasonic welding. Journal of Manufacturing Science and Engineering. 2019. Vol. 141. No. 8. DOI: https://doi.org/10.1115/1.4043731.
  • 15. Aliev, T. Noise control of the beginning and development dynamics of accidents. New York: Springer. 2019. 201 p.
  • 16. Aliev, T.A. & Musaeva, N.F. & Suleymanova, M.T. Algorithms for indicating the beginning of accidents based on the estimate of the density distribution function of the noise of technological parameters. Automatic Control and Computer Science. 2018. Vol. 52. No. 3. P. 231-242. DOI: https://doi.org/10.3103/S0146411618030021.
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5102153e-49eb-4fa5-9810-5211487c764f
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