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A novel replacement policy for a linear deteriorating system using stochastic process with dependent components
Author(s) -
Poladova Aynura,
Tekin Salih,
Khaniyev Tahir
Publication year - 2019
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.2494
Subject(s) - preventive maintenance , process (computing) , variance (accounting) , stochastic process , computer science , function (biology) , state (computer science) , mathematical optimization , stochastic modelling , control theory (sociology) , reliability engineering , mathematics , statistics , engineering , algorithm , control (management) , artificial intelligence , operating system , business , accounting , evolutionary biology , biology
In this study, a mechanical system with linear deterioration and preventive maintenance is considered. The state of the system over time is represented by a semicontinuous stochastic process with dependent components. The system cycles through on and off periods during its lifetime. The state of the system deteriorates linearly as a function of the usage time during on periods. When the system is offline, preventive maintenance is conducted, which improves the system state by a random amount. The system's on and off times and random improvement amounts are assumed to have general distributions. For such a system, our objective is to determine the expected value and variance for the number of preventive maintenance activities needed during the system lifetime and to propose a novel replacement policy for the system based on delay‐time modeling. Finally, the effectiveness of the obtained asymptotic results and the proposed replacement policy are tested through simulation.