Modeling a random sequence of extremes of loads for fatigue tests under irregular loading

2020 ◽  
pp. 13-19
Author(s):  
N.A. Mahutov ◽  
I.V. Gadolina ◽  
S.G. Lebedinskiy ◽  
E.S. Oganyan ◽  
A.A. Bautin

Methods and approaches to tests under random loading are considered, their role is characterized. To ensure the random nature of loading, a modeling method based on Markov transition matrices and real processes recorded in operation is proposed. Keywords: random loading process, Markov repetition matrices, resource estimation, corrected linear hypothesis, parameter of completeness of the loading spectrum. [email protected]

1999 ◽  
Vol 17 (2) ◽  
pp. 253-274 ◽  
Author(s):  
Joe H. Sullivan ◽  
William H. Woodall

2016 ◽  
Vol 24 (2) ◽  
pp. 497-503 ◽  
Author(s):  
Mohamed El Yazid Boudaren ◽  
Wojciech Pieczynski

Author(s):  
Joaquim AP Braga ◽  
António R Andrade

This article models the decision problem of maintaining railway wheelsets as a Markov decision process, with the aim to provide a way to support condition-based maintenance for railway wheelsets. A discussion on the role of the railway wheelsets is provided, as well as some background on the technical standards that guide maintenance decisions. A practical example is explored with the estimation of Markov transition matrices for different condition states that depend on the wheelset diameter, its mileage since last turning action (or renewal) and the damage occurrence. Bearing in mind all the possible maintenance actions, an optimal strategy is achieved, providing a map of best actions depending on the current state of the wheelset.


2014 ◽  
Vol 62 ◽  
pp. 731-736 ◽  
Author(s):  
J.L. Torres ◽  
M. de Blas ◽  
L.M. Torres ◽  
A. García ◽  
A. de Francisco

1997 ◽  
Vol 21 (4) ◽  
pp. 223-227 ◽  
Author(s):  
Stéphane Audic ◽  
Jean-Michel Claverie

Author(s):  
Ojin Kwon ◽  
Yong-Jin Yoon ◽  
Seung Ki Moon ◽  
Hae-Jin Choi ◽  
Joon Hyung Shim

2002 ◽  
Vol 39 (01) ◽  
pp. 91-99 ◽  
Author(s):  
Peter Eichelsbacher ◽  
Ayalvadi Ganesh

We consider the estimation of Markov transition matrices by Bayes’ methods. We obtain large and moderate deviation principles for the sequence of Bayesian posterior distributions.


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