Approach for Creating Reference Signals for Detecting Defects in Diagnosing of Composite Materials

  • Artur ZaporozhetsEmail author
  • Volodymyr Eremenko
  • Volodymyr Isaenko
  • Kateryna Babikova
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1080)


The article describes the approach to the formation of a simulation model of information signals, which are typical for objects with different types of defects. The dispersive analysis of the signal spectrum components in the bases of the discrete Hartley transform and the discrete cosine transform is carried out. The analysis of the form of the reconstructed information signal is carried out depending on the number of coefficients of the spectral alignment in Hartley bases and cosine functions. The basis of orthogonal functions of a discrete argument is obtained, which can be used for the spectral transformation of information signals of a flaw detector. A function was obtained approximating the distribution of the values of each of the coefficients of the spectral decomposition depending on the degree of damage (defectiveness) of the sample under study. It proved the need to use splines in the approximation of equations. The advantage of the splines is obtaining reliable results even for small degrees of interpolation equations, moreover, the Runge phenomenon does not arise, which occurs when using high-order polynomial interpolation.


Diagnostic signal Information signal Composite material Dispersion analysis Low-speed impact method Diagnosing Equation approximation Splines 


  1. 1.
    Lee, M., Thomas, C.E., Wildes, D.G.: Prospects for in-process diagnosis of metal cutting by monitoring vibration signals. J. Mater. Sci. 22(11), 3821–3830 (1987). Scholar
  2. 2.
    Widolo, A., Kim, E.Y., Son, J.-D., Yang, B.-S., Tan, A.C.C., Gu, D.-S., Choi, B.-K., Mathew, J.: Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine. Expert Syst. Appl. 36(2, Part 2), 7252–7261 (2009). Scholar
  3. 3.
    Peng, Z.K., Chu, F.L.: Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech. Syst. Signal Process. 18(2), 199–221 (2004). Scholar
  4. 4.
    Yan, R., Gao, R.X., Chen, X.: Wavelets for fault diagnosis of rotary machines: a review with applications. Sig. Process. 96(Part A), 1–15 (2014). Scholar
  5. 5.
    Sun, W., Shao, S., Zhao, R., Yan, R., Zhang, X., Chen, X.: A sparse auto-encoder-based deep neural network approach for induction motor faults classification. Measurement 89, 171–178 (2016). Scholar
  6. 6.
    Zaporozhets, A., Eremenko, V., Serhiienko, R., Ivanov, S.: Methods and hardware for diagnosing thermal power equipment based on smart grid technology. In: Advances in Intelligent Systems and Computing III, vol. 871, pp. 476–492 (2019).
  7. 7.
    Zaporozhets, A.A., Eremenko, V.S., Serhiienko, R.V., Ivanov, S.A.: Development of an intelligent system for diagnosing the technical condition of the heat power equipment. In: 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), Lviv, Ukraine, 11–14 September 2018.
  8. 8.
    Ali, Y.H., Rahman, R.A., Hamzah, R.I.R.: Acoustic emission signal analysis and artificial intelligence techniques in machine condition monitoring and fault diagnosis: a review. Jurnal Teknologi 69(2), 121–126 (2014)CrossRefGoogle Scholar
  9. 9.
    Sikdar, S., Kudela, P., Radzienski, M., Kundu, A., Ostachowicz, W.: Online detection of barely visible low-speed impact damage in 3D-core sandwich composite structure. Compos. Struct. 185, 646–655 (2018). Scholar
  10. 10.
    Babak, V., Mokiychuk, V., Zaporozhets, A., Redko, O.: Improving the efficiency of fuel combustion with regard to the uncertainty of measuring oxygen concentration. Eastern-Eur. J. Enterp. Technol. 6(8), 54–59 (2016). Scholar
  11. 11.
    Wu, Z., Huang, N.E.: Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal. 01(2), 1–41 (2009). Scholar
  12. 12.
    Song, Y., Fellouris, G.: Asymptotically optimal, sequential, multiple testing procedures with prior information on the number of signals. Electron. J. Stat. 11(1), 338–363 (2017). Scholar
  13. 13.
    Hsue, W.-L., Chang, W.-C.: Real discrete fractional Fourier, Hartley, generalized Fourier and generalized Hartley transforms with many parameters. IEEE Trans. Circuits Syst. I Regul. Pap. 62(10), 2594–2605 (2015). Scholar
  14. 14.
    Hsue, W.-L., Chang, W.-C.: Multiple-parameter real discrete fractional Fourier and Hartley transforms. In: 2014 19th International Conference on Digital Processing, Hong Kong, China, 20–23 August 2014.
  15. 15.
    Zaporozhets, A.: Analysis of control system of fuel combustion in boilers with oxygen sensor. Periodica Polytech. Mech. Eng. (2019).
  16. 16.
    Dertimanis, V.K., Spiridonakos, M.D., Chatzi, E.N.: Data-driven uncertainty quantification of structural systems via B-spline expansion. Comput. Struct. 207, 245–257 (2018). Scholar
  17. 17.
    Andrews, R.W., Reed, A.P., Cicak, K., Teufel, J.D., Lehnert, K.W.: Quantum-enabled temporal and spectral mode conversion of microwave signals. Nat. Commun. 6, 10021 (2015). Scholar
  18. 18.
    Jung, Y., Cho, H., Lee, I.: MPP-based approximated DRM (ADRM) using simplified bivariate approximation with linear regression. Struct. Multi. Optim. 59(5), 1761–1773 (2019). Scholar
  19. 19.
    Qu, Y., Wang, W., Guo, R., Ayhan, B., Kwan, C., Vance, S., Qi, H.: Hyperspectral anomaly detection through spectral unmixing and dictionary-based low-rank decomposition. IEEE Trans. Geosci. Remote Sens. 56(8), 4391–4405 (2018). Scholar
  20. 20.
    Zaporozhets, A.O., Redko, O.O., Babak, V.P., Eremenko, V.S., Mokiychuk, V.M.: Method of indirect measurement of oxygen concentration in the air. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu (5), 105–114 (2018).
  21. 21.
    Babak, S., Babak, V., Zaporozhets, A., Sverdlova, A.: Method of statistical spline functions for solving problems of data approximation and prediction of objects state. In: CEUR Workshop Proceedings, vol. 2353, pp. 810–821 (2019).
  22. 22.
    Ali, A., Khan, K., Haq, F., Shah, S.I.A.: A computational modeling based on trigonometric cubic B-spline functions for the approximate solution of a second order partial integro-differential equation. In: New Knowledge in Information Systems and Technologies. Advances in Intelligent Systems and Computing, WorldCIST 2019, vol. 930, pp. 844–854 (2019).
  23. 23.
    Eremenko, V., Zaporozhets, A., Isaenko, V., Babikova, K.: Application of wavelet transform for determining diagnostic signs. In: CEUR Workshop Proceedings, vol. 2387, pp. 202–214 (2019).
  24. 24.
    Han, X., Guo, X.: Cubic Hermite interpolation with minimal derivative oscillation. J. Comput. Appl. Math. 331, 82–87 (2018). Scholar
  25. 25.
    Meshram, S.G., Powar, P.L., Meshram, C.: Comparison of cubic, quadratic, and quintic splines for soil erosion modeling. Appl. Water Sci. 8, 173 (2018). Scholar
  26. 26.
    Zaporozhets, A.: Development of software for fuel combustion control system based on frequency regulator. In: CEUR Workshop Proceedings, vol. 2387, pp. 223–230 (2019).
  27. 27.
    Brajovic, M., Orovic, I., Dakovic, M., Stankovic, S.: On the parameterization of Hermite transform with application to the compression of QRS complexes. Sig. Process. 131, 113–119 (2017). Scholar
  28. 28.
    Zaporozhets, A.O., Eremenko, V.S., Isaenko, V.M., Babikova, K.O.: Methods for creating reference signals for the diagnosis of composite materials. In: Proceedings of International Scientific Conference Computer Sciences and Information Technologies (CSIT-2019), vol. 1, pp. 84–87 (2019)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Engineering Thermophysics of NAS of UkraineKievUkraine
  2. 2.Igor Sikorsky Kyiv Polytechnical InstituteKievUkraine
  3. 3.National Aviation UniversityKievUkraine

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