Jul 2026· Proceedings of the Southwest State University· 0 citations· 15 references
Abstract
Purpose of research
. The purpose of research is to develop and substantiate a logic-probabilistic model for managing maintenance and repair processes of ship technical systems, taking into account the stochastic dependence between component failures using copula theory. The study also aims to quantitatively assess the impact of external operational factors on equipment reliability and on the accuracy of critical failure prediction
Methods
. The study applies the logic-probabilistic reliability analysis method (fault tree analysis) to formalize the structure of a ship system and describe the conditions leading to a critical system failure. To model statistical dependence between failures of interconnected components, the Clayton copula is employed, enabling the representation of strong lower-tail dependence. Failure probabilities of individual components are determined based on the exponential time-to-failure distribution. A corrective function reflecting the impact of exogenous factors (humidity, salinity and vibrational load) is incorporated into the model. Empirical validation is performed using operational data from 15 vessels of Project 22870 for the period 2020-2024, applying absolute and relative error metrics.
Results
. The results show the accounting for the dependence between the cooling pump and the generator increases the estimate annual probability of a critical system failure from 28.11% to 37.04% (a 31.77% relative increase). The probability of their joint annual failure reaches 17.52%, which is more than twice the estimate obtained under the assumption of statistical independence. Model validation demonstrates a deviation of less than 2.88% from observed data.
Conclusion
. The proposed model provides a more accurate assessment of operational failure risks and can serve as a tool for adaptive maintenance planning, as well as integration with digital twins and predictive maintenance systems in the shipbuilding industry.
The article presents a study of the engine reliability of a new generation of trucks manufactured by a Russian manufacturer during the warranty period. The study is based on a statistical analysis of the mean time between failures of units, systems, and components. The components under study are ranked by mean time between failures, failure rate, and risk of damage from failure. The two-parameter Weibull distribution was used to estimate the mean time between failures distribution parameters, and the model goodness of fit was tested using the Hollander—Proschan, Mann—Scheuer—Fertig, and Anderson—Darling criteria. Data were obtained on the frequency of engine component maintenance at different levels of failure-free operation depending on the failure hazard criterion — the risk of damage from failure. A cluster analysis using the K-means method was performed, which made it possible to identify three homogeneous groups of components based on the frequency of maintenance: low-service (up to 50,000 km), medium-service (70,000—130,000 km), and high-service (over 150,000 km). The results can be used to optimize maintenance schedules, plan maintenance and repair costs, and improve the overall reliability of the vehicle fleet.
F. L. Nazarov, A. A. Arhireyskiy, R. Kalimullin· Truck· 0 citations
Reliability modeling in repairable systems frequently relies on binary assumptions of perfect or minimal repair, creating an analytical gap by overlooking continuous degradation in critical assets under severe wear. Given the risk of mission failure and the imperative to ensure operational readiness, this study aimed to develop an imperfect repair stochastic model based on the Generalized Renewal Process integrated with stochastic degradation modeling (GRP–Weibull). The approach applies Kijima’s virtual age theory (Type II) to evaluate the impact of doctrine-defined maintenance echelons on the conditional reliability and operational availability of defense systems. The methodology comprised Maximum Likelihood Estimation (MLE) of failure data implemented in R software, followed by Monte Carlo Simulation to project system behavior after preventive interventions. The results demonstrated that the model accurately quantifies asset rejuvenation via the parameter (ρ), enabling the determination of the optimal maintenance periodicity before reliability reaches critical thresholds. This research addresses methodological gaps in classical reliability engineering, establishing a robust predictive logistics tool to sustain operational availability.
Cleunis Brandão Barros, Adonias Magdiel Silva Ferreira, Delano Mendes de Santana et al.· Revista de Estudos Interdisc...· 0 citations
An urgent scientific and practical problem of transitioning from time-based preventive maintenance of hydraulic drives to condition-based predictivemaintenance within the "digital twin" framework has been resolved. The authors have substantiated the limitations of classical static reliability analysismethods that operate on Boolean logic and the exponential failure distribution law, completely ignoring the chronological sequence of degradationprocesses and the accumulation of micro-damage in precision pairs and working fluid. A fundamentally new hybrid methodology for evaluating thereliability of hydraulic systems is proposed, which integrates structural-logical schemes of dynamic fault trees, stochastic modeling of wear physicsbased on the Weibull distribution, and a real-time Bayesian parameter update mechanism using telemetry subsystem data. The scientific novelty of theapproach lies in the mathematical formalization of time-dependent degradation processes and the cascade failure effect by adapting specialized logicaloperators and functional dependency triggers. This enabled the consideration of conditional failure probability, where the output event is activated onlyunder a specific sequence of physical phenomena over time. The efficiency of the developed four-level architecture of the predictive diagnosticsalgorithm was validated through testing on a virtual test rig of a 6-ton vertical load holding actuator built using Bosch Rexroth precision components.It has been experimentally proven that when the monitoring system detects an anomalous pressure drift, the dynamic model with Bayesianrecalculation of shape and scale parameters avoids the "optimistic error" of classical analysis. At 2,000 hours of operating time, the model captured a2.4-fold increase in individual failure risk (up to 18 % compared to 7.5 % in the static model). The application of the algorithm provided adaptiveadjustment of the remaining useful life forecast from the nominal 4,200 hours to the actual 1,100 hours, enabling timely unscheduled maintenance andpreventing critical damage to hydraulic units, the rectification cost of which is 5–7 times higher than preventive component replacement.
Nadiia Fatieieva, Oleksandr Fatyeyev, Valerii Poliakov et al.· Bulletin of the National Tec...· 0 citations
This study investigates predictive modeling of failure states in manufacturing systems using artificial intelligence in the context of sustainable maintenance and Industry 4.0 to point to the potential of integrating AI-supported maintenance as a tool for increasing the reliability and sustainability of manufacturing systems.
Miroslav Rakyta, Peter Bubeník, Vladimíra Biňasová et al.· Electronics· 1 citation
This document proposes a methodology for enhancing the scheduling of maintenance activities for railway infrastructure using deterioration and optimisation models. The approach combines a deterioration model with a semi-Markov model through the degradation variable (Weibull distribution). The objective is to determine the optimal risk level that triggers maintenance interventions to achieve the most significant economic benefits. The creation of digital twins for both assets and infrastructures facilitates the use of real-time operational data and enables the implementation of analytical models to support maintenance decision-making. Organisations identify and collect relevant asset data, convert this data into model inputs and subsequently use the model outputs to evaluate and select the most advantageous maintenance strategies. This methodology advances current railway decision-support systems for preventive maintenance, transitioning from a traditional table-based approach (subjectively defined risk levels) to an objective method that identifies the risk level which triggers the maintenance intervention based on economic factors. The model can be applied to a wide range of railway assets and is designed to be integrated into a digital twin system. A case study demonstrates its application to the management of track sections. The paper highlights the critical role of data-driven approaches in achieving economically efficient maintenance strategies, while also acknowledging that other key factors in the railway sector, such as safety and transport efficiency, complement these benefits.
Alfonso Carballo-Menayo, A. Sánchez-Herguedas, A. Márquez· Proceedings of the Instituti...· 0 citations
Designing and implementing a preventive maintenance plan in the Ecuadorian mining industry requires systematic methodologies to reduce failures in crushing and grinding machines, improving operational availability and productivity. The main problem identified is reliance on corrective maintenance, with no historical failure records, which causes unexpected shutdowns. This study applies the FMEA (Failure Mode and Effects Analysis) methodology to evaluate the criticality of equipment using the Risk Priority Number (NPR), complemented with the Weibull distribution to model useful lifetimes. Key equipment such as the ball mill and jaw crusher at a mining beneficiation plant were analyzed. The results indicate that 25% of the subcomponents have NPR > 300 (unacceptable risk), mainly concentrated in structural elements subjected to severe operating conditions. A scenario analysis based on the fitted Weibull models and on reductions reported in the literature projects that implementation of the proposed preventive plan would reduce unplanned shutdowns by 40–50%, raising operational availability from the current 73% to approximately 90%; these figures are model-based projections and not post-implementation measurements. Weibull integration provides a robust statistical framework for the transition from reactive maintenance to predictive schemes, optimizing intervention intervals and extending the life of critical assets.
J. D. B. Enriquez, Carlos David Amaya-Jaramillo, Danny Alexander Rivas Sierra et al.· Journal of Technology Inform...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
