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Evaluation of the maintenance system readiness using the semi-Markov model taking into account hidden factors
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Lublin University of Technology, Poland
Military University of Technology, Poland
War Studies University, Poland
Submission date: 2023-07-25
Final revision date: 2023-08-31
Acceptance date: 2023-09-23
Online publication date: 2023-09-24
Publication date: 2023-09-24
Corresponding author
Edward Kozłowski   

Lublin University of Technology, Poland
Eksploatacja i Niezawodność – Maintenance and Reliability 2023;25(4):172857
  • A competitive model for predicting the readiness of the maintenance system has been developed using the semi-Markov model.
  • The method of using the Semi-Markov model in a complex system has been presented.
  • The method of estimating the parameters of the semi-Markov model has been presented in a situation where the sojourn time distributions in the given state are not identifiable using one of the classical distributions.
  • Diagnostics and evaluation of a transport company in terms of its readiness have beenmade
Modelling the time that the system remains in a given state using classical distributions is not always possible. In many cases, empirical distributions are multimodal due to the influence of external, hidden factors and the selection of the best classical distributions may lead to erroneous results. In the article the method of diagnosis of influence of hidden factors into sojourn time of semi-Markov models was presented. In order to capture hidden factors, the authors proposed to model the distributions of the sojourn time with a mixture of distributions, which is a significant novelty in relation to the studies presented in the literature. Hidden factors directly affect the reliability of technical systems. Detecting the existence of these factors enables more accurate modeling of system readiness. Paying attention to irregularities caused by hidden factors makes it possible to reduce system maintenance costs. Such a system model provides complete information and enables a reliable assessment of the system readiness and maintenance.