This study develops a simulation-based framework that connects a corrected three-state fault-transition model, a sequence of myopic state-conditioned zero-sum attack-defence games and node-level resource allocation for smart mining cyber-physical systems. Normal, faulty and recovering states are separated by explicit entry, recovery and relapse rules. At three decision stages, the current state determines a complete 10 × 10 success-probability and payoff matrix, from which the attacker and defender mixed strategies are recomputed by linear programming. A deterministic convex benchmark then minimises total resource use subject to a raw-capability floor; a genetic algorithm is retained as a numerical benchmark. For the representative 107-node configuration, the deterministic allocation uses 67.3313 units to attain the required capability of 0.52, saving 32.7% relative to the 100-unit baseline. Across 30 genetic-algorithm runs, every run is feasible, the mean resource use is 67.4365 ± 0.0061 units and the mean gap from the deterministic optimum is 0.156%. The framework therefore supports state-conditioned strategy analysis and auditable resource planning.
To inherit the merit of Sequential Space Conversion (SESC) that can avoid geometrically misleading of the LSF during failure probability estimation and enhance its efficiency, a deeply coupled adaptive Kriging-based SESC method is proposed. Apart from an initial global enrichment, the Kriging model can be locally enriched in each simulation level of SESC. Since each level of MCMC samples progressively approaches the failure region, Kriging model locally updated using those as testing samples can capture more information close to the failure boundary and benefits the performance of the surrogate-assisted simulation method. To keep spatial consistency of the sequence of local enrichment in each level of converted space in SESC, an improved adaptive scheme with a modified U-learning function is proposed as well. By tightly integrating the adaptive Kriging refinement with the SESC progression, the proposed method not only inherits the advantage of SESC in avoiding geometrically misleading failure region identification, but also preserves spatial consistency across successive transformed spaces, thereby achieving accurate failure probability estimation with substantially reduced computational cost. The effectiveness of the proposed method was confirmed by conducting three numerical examples and a test on a wind turbine blade under extreme wind condition.
Reconfigurable manufacturing systems (RMSs) have gained prominence due to their ability to adapt to changing market demands and product varieties. However, maintenance planning in such systems remains challenging because of their dynamic structures, modularity, and evolving operational conditions. This paper presents a systematic literature review on maintenance scheduling in RMSs, exploring key reconfigurability characteristics and their impact on maintenance operations, with the aim of synthesizing existing research and identify key modeling and decision-making challenges. Based on these insights, a dynamic grouping–based maintenance framework is proposed, explicitly tailored to address the challenges inherent in RMS environments. The framework links RMS core characteristics (modularity, scalability, customization, and convertibility) to a dynamic grouping maintenance strategy. The applicability of the framework is demonstrated through representative scenarios reflecting dynamic demand, capacity variation, and reconfiguration-driven maintenance opportunities. Results show that this dynamic grouping-based framework not only reduces maintenance cost while preserving system responsiveness, but also functions as an adaptive maintenance policy capable of continuously adjusting maintenance decisions to structural changes and operational variability in RMS environments.
Pre-Start-Up Safety Review (PSSR) is routinely used as a compliance checklist, but its records are rarely converted into quantitative barrier-layer measures or linked to start-up performance. This study develops a risk-informed framework that uses PSSR maturity as a multidimensional proxy for pre-operational barrier integrity. Its novelty lies in integrating maturity measurement, barrier-gap diagnosis, risk-based prioritization, and empirical assessment of commissioning stability. A PSSR Maturity Index (PMI) was constructed across five dimensions: design conformity, mechanical completion, safeguard readiness, procedural closure, and personnel competency. The framework was evaluated using 350 PSSR evaluations from commissioning of a Heat Recovery Steam Generator at a combined-cycle power plant. Associations with stable start-up were examined using multivariable logistic regression, bootstrap resampling, and 10-fold cross-validation. Safeguard readiness (OR = 1.86) and mechanical completion (OR = 1.58) showed the strongest associations, whereas procedural closure was not independently associated with stability. The model showed modest explanatory capacity but limited discrimination (Nagelkerke pseudo- R 2 = 0.163; AUC = 0.624), indicating that the readiness dimensions captured partial rather than comprehensive signals of commissioning vulnerability. The framework’s primary contribution is diagnostic: it distinguishes documentary completion from functional barrier readiness and converts routine PSSR evidence into barrier-specific gaps and risk-ranked corrective priorities. Although the findings are case-specific, the framework provides a reproducible basis for cross-site validation.
Selective maintenance strategy has been introduced to scientifically optimize maintenance planning and enhance the effectiveness and feasibility of maintenance solutions, thereby identifying the optimal maintenance program that ensures the rational allocation of resources. This paper considers a selective human-robot collaborative maintenance model based on expected profit and worker fatigue accumulation, taking into account the risk of maintenance task failure. Additionally, a Genetic Engineering with Experience Replay Algorithm (GEERA) is developed, which seamlessly integrates the capabilities of the experience replay mechanism to retain dominant individuals and locally identify the optimal solution, along with the global exploration and optimization capabilities of the Genetic Engineering Algorithm (GEA) to explore and optimize globally. Moreover, the rationality of the model and the practical effectiveness of the algorithm are validated through an actual maintenance case study. Based on 20 independent runs, GEERA increases NPS by approximately 16.88% and reduces IGD by 14.35% compared with the best-performing benchmark, while also achieving better HV and Spread values. These results demonstrate its effectiveness in providing high-quality maintenance alternatives for balancing maintenance benefits and worker fatigue.
