Sep 2026· Proceedings of the Institution of Mechanical Engineers. Part O, Journal of risk and reliability· 0 citations· 15 references
Abstract
Maintenance outcomes in real-world industrial settings are inherently stochastic, varying among better-than-minimal, minimal, and worse-than-minimal effects due to factors such as technician expertise, environmental conditions, and component wear. Traditional repairable system models—including the Non-Homogeneous Poisson Process (NHPP), Generalized Pólya Process (GPP), and Extended Generalized Pólya Process (EGPP)—rely on deterministic repair assumptions that fail to capture this variability, leading to inaccurate reliability predictions and suboptimal maintenance decisions. To overcome these limitations, this paper proposes a hybrid model incorporating EGPP, NHPP, and GPP within a Discrete-Time Markov Chain (DTMC) framework. The model dynamically represents probabilistic transitions between repair effects via a state-dependent transition matrix and integrates them into a weighted mixed failure rate function, reflecting the true evolution of system reliability. Based on this model, a periodic replacement strategy is developed to minimize long-term cost rates. Experimental validation confirms that the proposed approach significantly enhances predictive accuracy and cost efficiency compared to conventional models, offering a more realistic and adaptable foundation for maintenance optimization in industrial applications.
A new non-homogeneous Markov process (NHMP)-based framework for software reliability modeling under imperfect debugging is developed and evaluated, showing that the proposed framework often improves goodness-of-fit, while its predictive advantage is more limited.
Geometric process (GP) models are widely used for repairable systems whose inter-failure times evolve over successive failure-repair cycles. Maintenance decisions under GP models are often made with limited failure data and are subject to uncertainty in baseline selection and the resulting maintenance decision. Existin...
Wen-Hui Wu, Bing Wang, Wei Wang et al.· 0 citations
Finite-buffer production systems are widely used in manufacturing, where performance depends on the interaction among machine reliability, buffer capacity, and maintenance policies. This study proposes a unified Continuous-Time Markov Chain (CTMC) framework for modeling and comparatively evaluating alternative finite-b...
Angelos Kourepis, N. Kladovasilakis, Elias D. Georgakoudis et al.· Journal of Manufacturing and...· 0 citations
The purpose of this paper is to propose and systematically evaluate coordinated maintenance policies for multi-component systems undergoing stochastic degradation in the presence of visit-level economic dependencies. By explicitly incorporating condition monitoring and system-level decision mechanisms, the study ai...
K. Cheikh, El Mostapha Boudi· Journal of Quality in Mainte...· 0 citations
Modern systems incorporate an increasing number of components performing complex functions, necessitating the development of adequate mathematical models for their reliability. Particular attention should be paid to mission-critical units, whose failure may lead to the breakdown of the entire system. Such priority unit...
Yuriy E. Obzherin, Stanislav M. Sidorov· International Journal of Rel...· 0 citations
This paper addresses the maintenance optimization of a parallel system comprising two components subject to stochastically dependent degradation processes. The focus is placed on the scenario of partial observability, where the condition of only one component is observed/observable. The degradation of the components is...
N. Esposito, L. Bautista, Massimiliano Giorgio et al.· Methodology and Computing in...· 0 citations
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