On the Possibility of Using the Method of Sign-Perturbed Sums for the Processing of Dynamic Test Data
- 3 Downloads
At the present time, the methods for the measurement and prediction of the dynamic strength of materials are complicated and unstandardized. An experimental data processing method based on the incubation time criterion is considered. Only a finite number of measurements containing random errors and limited statistical information are usually available in practice, since dynamic tests are laborious, and every individual test requires a lot of time. This strongly restricts the number of applicable data processing methods unless we are satisfied with approximate and heuristic solutions. The method of sign-perturbed sums (SPS) is used for the estimation of finite-sample confidence regions with a specified confidence probability under the assumption of noise symmetries. It is shown that several experimental points are sufficient to determine the strength parameter with an accuracy acceptable for engineering calculations. The applicability of the proposed method is demonstrated in the processing of a number of experiments on the dynamic fracture of rocks.
Keywordssign-perturbed sums dynamic fracture incubation time
Unable to display preview. Download preview PDF.
- 3.D. A. Shockey, L. Seaman, and D. R. Curran, Material Behavior under High Stress and Ultrahigh Loading Rates (Springer-Verlag, New York, 1983).Google Scholar
- 5.V. S. Nikiforovsky and E. I. Shemyakin, Dynamic Fracture of Solids (Nauka, Novosibirsk, 1979) [in Russian].Google Scholar
- 7.J. F. Kalthoff and S. Winkler, “Failure mode transition at high rates of shear loading,” in Proc. Int. Conf. on Impact Loading and Dynamic Behavior of Materials, Bremen, 1987 (DGM-Informationsgesellschaft, Oberursel, 1988), pp. 161–176.Google Scholar
- 19.M. Volkova, G. Volkov, O. Granichin, and Y. Petrov, “Sign-perturbed sums approach for data treatment of dynamic fracture tests,” in Proc. 56th IEEE Conf. on Decision and Control (CDC), Melbourne, Australia, Dec. 12–15, 2017 (IEEE, Piscataway, NJ, 2018), pp. 1652–1656. ## http://ieeexplore.ieee.org/document/8263887/Google Scholar
- 20.A. Senov, K. Amelin, N. Amelina, and O. Granichin, “Exact confidence regions for linear regression parameter under external arbitrary noise,” in Proc. 2014 American Control Conference (ACC 2014), Portland, OR, June 4–6, 2014 (IEEE, Piscataway, NJ, 2014), pp. 5097–5102.Google Scholar
- 21.A. A. Senov and O. N. Granichin, “Identification of linear regression parameters for arbitrary external noise in observations,” in Proc. 12th All-Russ. Meeting on Control Problems (VSPU-2014), Moscow, June 16–19, 2014 (Inst. Probl. Upr. im. V.A. Trapeznikova, Moscow, 2014), pp. 2708–2719.Google Scholar