Short Title: Int. J. Mech. Eng. Robot. Res.
Frequency: Bimonthly
Professor of School of Engineering, Design and Built Environment, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
2025-04-02
2025-02-28
2025-01-20
Manuscript received October 24, 2024; revised December 25, 2024; accepted January 15, 2025; published April 14, 2025
Abstract—This research considers challenges and importance of accurately computing statistical distribution characteristics in the context of technical systems testing, particularly for fatigue and endurance tests of structural elements in complex systems like turbines and engines. These accurate computations are crucial for developing digital twins of equipment as traditional approximations often fall short in providing the necessary precision. Key distributions mentioned include the non-central student distribution, the distribution of the variation coefficient, and order statistics all of which are essential for justifying tolerance intervals and assessing the life characteristics of critical components. The article highlights the computational difficulties posed by infinite integration limits and the need for minimizing target functions, which complicate accurate calculations. To address these issues the article presents developed JavaScript algorithms and software designed to compute the required distribution characteristics for a broad range of continuous distributions lacking satisfactory approximations. These solutions aim to facilitate faster and more precise computations for engineering tasks. Additionally, the article discusses some of the most accurate approximations available for these distributions. Keywords—digital twin, technical condition of equipment, inverse distribution functions, student distributionCite: Agamirov L. Vladimirovich, Oparin M. Vitalievich, Chechetkin D. Andreevich, Kislitsyn M. Andreevich, and Muhammad Maaz Shaikh, "Algorithms and Software for Computing Inverse Distribution Functions in Power Equipment Based on Digital Twins," International Journal of Mechanical Engineering and Robotics Research, Vol. 14, No. 2, pp. 184-190, 2025. doi: 10.18178/ijmerr.14.2.184-190Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
