Open AccessRandom processes imitation in the problem of fatigue under variable loading
Author(s) -
Н. А. Махутов,
И. В. Гадолина,
Nelly S. Dinyaeva
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1777/1/012014
Subject(s) - maxima and minima , random variable , sequence (biology) , random function , mathematics , stochastic process , algorithm , concatenation (mathematics) , function (biology) , random sequence , random element , computer science , statistical physics , statistics , mathematical analysis , combinatorics , physics , distribution (mathematics) , genetics , evolutionary biology , biology
There is strong evidence, that the extrema and their sequence play a major role in metal fatigue. Due to this random testing with extrema control might be preferable, compared with the testing with generating by the spectral densities. The method is proposed allowing to generate random processes with the stable integral characteristics of cumulative distributions, and at the same time, being random by their nature. The algorithm consists of two steps. The first one is creating the random sequence of extrema, basing on the target Markov matrix, filled up on the base of the loading process, recorded during exploitation. The next step is the creation of the random process with its continuous first derivate. The special concatenation procedure was proposed and had been proved to create the continuous function reconstruction out of the point-wise specification (local maximums and minimums sequence). To judge the frequency content a regression analysis of two random variables was performed. The obtained continuous process might be undergone by the spectral analysis to get the spectral density function, which might be required in applying some methods for random fatigue estimation. The main trait of the proposed method is the coincidence of the rain-flow cycle counting characteristics of the initial and the resulting processes.