The present study proposes a multi-criteria approach for prioritizing failure modes in naval systems, with the aim of improving decision-making for sustainable maintenance strategies. It builds upon a previous conference paper that introduced a framework for failure modes prioritization in autonomous ship navigation systems. The analysis is conducted through two parallel paths. The Analytic Hierarchy Process (AHP) is first independently applied to determine the relative importance of criteria and to produce a complete ranking of failure modes. Secondly, the AHP is combined with the ELimination Et Choix Traduisant la REalité I (ELECTRE I) method with the goal to integrate pairwise weighting with the outranking logic, thus obtaining an alternative prioritization. The criteria set includes both traditional Failure Mode, Effects and Criticality Analysis (FMECA) dimensions, that are Severity, Occurrence and Detection, and some additional ones that characterize the specific scenario, that are Economic Factor (EC), Sustainability Maintenance Strategies (SMS) and Management and Data Security (MDS). The first-ranked failure mode from AHP standalone analysis is compared with the top-ranked result of the integrated AHP+ELECTRE I approach to highlight analogies and discrepancies. Finally, a sensitivity analysis is performed to evaluate the robustness of the findings.

This study investigates the performance of goodness-of-fit tests for the ARA ∞ –PLP imperfect maintenance model, with a particular emphasis on entropy- and extropy-based test statistics. Test statistics are constructed using three different approaches: martingale residuals, probability integral transform, and information-based measures. Extensive simulation studies are conducted under several alternative hypotheses, including ARA 1 , ARA ∞ –LLP, QR, EGP, and Brown–Proschan models, to evaluate the empirical power of the proposed tests. In addition to numerical power comparisons, graphical analyses are employed to illustrate the behavior of the test statistics and to provide further insight into their sensitivity under different repair scenarios. The simulation results demonstrate that entropy- and extropy-based statistics generally outperform classical goodness-of-fit tests, particularly in detecting deviations from the null model under moderate and severe imperfect repair effects. The consistency observed between graphical patterns and numerical findings further confirms the robustness and interpretability of the proposed procedures. An application to a real dataset related to automobile failure times illustrates the practical effectiveness of the methodology and supports the suitability of the ARA ∞ –PLP model for real-world repairable systems.

Surrogate models are widely utilized in performance reliability analysis due to superior abilities in balancing computational cost and accuracy. However, few of existing relevant studies effectively integrate multi-fidelity information, leading to inefficiency in estimating small failure probabilities. To address this, a clustering-based surrogate modeling method under multiple failure modes is proposed, systematically incorporating both high- and low-fidelity data. Specifically, an active learning strategy guided by clustering selectively retains high-value sample points to promote surrogate accuracy during iteration. Besides, it integrates mixed-weight importance sampling to evaluate system failure probability while reflecting the contribution of individual failure modes, with optimal model parameters via particle swarm optimization algorithm. The proposed approach enhances the efficiency of small failure probability analysis by innovatively integrating clustering-based multi-fidelity data and quantifying failure mode interactions with mixed weights. Numerical and engineering studies demonstrate that PRA-MFCS achieves superior accuracy and efficiency compared to traditional channels, providing a reliable tool for the refined design of complex mechanical systems.

Optimal single sampling inspection plans with fixed acceptance numbers are developed to provide the appropriate protection to consumers when the lifetime of products follows a type-I half-logistic Nadarajah-Haghighi (TIHLNH) distribution. The best inspection plan using percentile life as a measure of reliability is determined when the conventional consumer risk is specified. Operating characteristic (OC) values for the different quality level options are reported. A minimum ratio between the true median life and the pre-specified life has been supplied for the specific producer’s risk. The optimal single sampling plans are then derived using prior knowledge on fraction defective by controlling the expected consumer risk in the Bayesian setting. The results show that the proposed Bayesian sampling plans is more efficient than the current sampling plans in terms of sample size. For illustrative purposes, the proposed methods are applied to a real data set.

Accurate prediction of the Remaining Useful Life (RUL) is crucial for avoiding unscheduled downtime, enhancing safety, and reducing maintenance costs. Traditional methods face challenges with high-dimensional, nonlinear, and uncertain data. This paper presents a framework based on Bayesian Additive Regression Trees (BART), integrating RUL prediction with feature selection. The model is trained and tested on the NASA CMAPSS dataset, identifying key sensor features through SHAP analysis. Results show that BART can achieve accurate predictions, reasonable uncertainty estimates, and effectively identify critical variables.

This paper presents a novel framework for evaluating the network reliability of stochastic flow networks (SFNs) by integrating fuzzy set theory to address the inherent uncertainty in lead time constraints. Traditional network reliability models typically assume deterministic lead times, which fail to capture the variability and imprecision encountered in real-world operational environments. To overcome this limitation, this research represents lead times as fuzzy numbers using triangular membership functions, thereby enabling a more realistic characterization of temporal uncertainty in network performance analysis. The proposed methodology employs α-cut operations at multiple confidence levels to transform fuzzy lead times into crisp intervals, generating both optimistic (bestcase) and pessimistic (worst-case) reliability scenarios for each α-level. By systematically evaluating the network across different confidence thresholds, the framework produces reliability intervals that reflect the full spectrum of uncertainty. Such SFNs serve as probabilistic models for analyzing system capacity. These fuzzy reliability results are subsequently converted into a single, actionable crisp value through the Center of Area (COA) defuzzification method, facilitating practical decision-making while preserving the richness of uncertainty information. The proposed approach offers significant advantages for network planning and resource allocation in contemporary infrastructure systems, including transportation, energy distribution, and communication networks, where operational parameters are subject to volatility and uncertainty. By acknowledging and quantifying inherent uncertainties while providing risk-aware insights through reliability intervals, this framework supports more robust and informed decision-making in dynamic operational environments.

Preventive maintenance (PM) policies for repairable systems are commonly modeled using static restoration assumptions, despite growing empirical evidence that maintenance effectiveness degrades with system age. This study develops a nonparametric, age-dependent preventive maintenance effectiveness framework for repairable systems operating under imperfect maintenance. Building upon a virtual-age nonhomogeneous Poisson process (NHPP) model with multiple PM types, the proposed approach estimates age-specific restoration factors directly from field data without imposing restrictive functional forms. The model is applied to a fleet of underground load–haul–dump (LHD) trucks using real-world failure and maintenance records from the mobile subsystem. Preventive maintenance intervals are subsequently optimized with the objective of maximizing system availability under operational feasibility constraints. Results demonstrate that maintenance effectiveness varies substantially across age bands and PM types, with light PM actions exhibiting greater sensitivity to aging effects than moderate or major interventions. The optimized age-dependent PM schedules differ systematically from static Original Equipment Manufacturer (OEM) policies, particularly in mid- and late-life operating regimes. These findings highlight the limitations of constant-effectiveness maintenance models and illustrate the practical value of incorporating age-dependent restoration behavior into preventive maintenance planning for complex industrial systems.

The microgrid system (MS) is composed of several infrastructures, including energy sources, converters, and feeders. Due to the increasing complexity and climate change, assessing grid reliability and identifying critical infrastructure have become critical issues for the operator. Accordingly, a reliability-based framework for identifying the critical infrastructure within the MS is proposed in this study. Grid reliability serves as a performance metric to measure the supply capability of the MS, which is defined as the possibility that the demand of the user can be met. Considering that infrastructures in the MS may operate at multiple state levels due to failures or maintenance, the power rating of these infrastructures should be viewed as multistate. Network analysis is employed in this study, a practical approach that considers the multistate power rating in the MS by constructing a stochastic microgrid network (SMN). Based on calculated grid reliability, the infrastructure importance analysis is introduced to systematically assess the impact of a complete failure of each infrastructure on grid reliability. An illustrative example is provided to demonstrate the feasibility of the proposed framework, assisting the operator in evaluating SMN grid reliability and identifying the critical infrastructure that significantly impacts SMN performance.

As a typical external disaster, earthquakes not only can trigger the failure of individual nuclear power equipment but may also lead to common-cause failures, thereby significantly increasing the risk of systemic failure. To assess seismic risk of multi-equipment common-cause failures, this paper constructs an integrated probabilistic analysis framework of “ground motion parameter-equipment response-system failure.” Based on a system failure probability modeling approach that considers failure dependencies, probabilistic risk parameters derived from seismic hazard analysis and seismic fragility assessments are integrated to achieve accurate quantification of multiequipment failures. Quantitative analysis of failure dependencies under varying ground motion intensities is conducted based on the α-factor model, revealing a significant nonlinear evolutionary pattern of failure correlation coefficients with respect to ground motion intensity. The rationality of the framework and methodology is validated through practical case studies. This research provides an analytical tool with both theoretical rigor and engineering applicability for seismic risk assessment in the nuclear engineering field, effectively enhancing the precision of probabilistic safety analysis for complex redundant systems.

Maintenance activities are fundamental to reducing equipment failures and improving operational efficiency. Despite their critical role, the impact of human error on the effectiveness of both preventive and corrective maintenance remains insufficiently examined in the existing literature. This study addresses this gap by introducing a comprehensive and dynamic framework that integrates a System Dynamics (SD) model for quantifying Human Error Probability (HEP) with an optimization model designed to minimize total maintenance costs. By explicitly incorporating human error into maintenance planning and execution, the proposed approach identifies optimal maintenance levels and human-error-related variables, supporting more accurate and practical decision-making. The framework was validated through real-world industrial data and further examined using sensitivity analysis, confirming its robustness and practical relevance. The results indicate that an optimal HEP of 0.0204 provides an effective balance between operational performance and cost reduction. These findings offer actionable insights for maintenance managers to design strategies resilient to human error, ultimately enhancing equipment reliability, reducing unplanned downtime, and improving organizational performance. This integrated SD–optimization framework provides a novel human-centered perspective on maintenance planning and addresses a significant gap in maintenance management research.