The presence of silica in iron ore concentrate can have significant negative impacts on the efficiency and quality of steel production. As such, providing engineers with early and reliable information about the purity of iron ore concentrate is crucial for smooth mining operations. This paper reports on the development of a long short-term memory (LSTM) network and an ensemble data interpolation technique to enhance quality prediction in the froth flotation process of an iron ore mine. Our results demonstrate the ability of our model to accurately predict the silica content of iron ore concentrate on a minute-by-minute basis, as well as the ability to forecast hours in advance.

The study presents a decision support model for optimizing aircraft maintenance plans, targeting repairable units with hidden functions. The model addresses the complexities of integrating multiple factors, such as aging, the availability of hidden functions during operation, the varying effectiveness of restoration and repair tasks, and the operational and economic consequences of failure.It considers a combination of inspections, restorations, and a discard task at the unit’s predefined renewal age, for the “non-safety effect” failure category. It incorporates the Kijima type II model to account for the effect of imperfect restoration and the concept of mean fractional dead time to identify the optimal intervals for inspections, the number of inspections needed within each restoration cycle, and the frequency of restorations in a renewal cycle. Real-world adapted numerical examples are used to demonstrate the model’s application. Sensitivity analysis further enhances the model’s utility by determining the influence of reliability and cost parameters on maintenance optimization outcomes. The results show that, in the absence of aging, restoration effectiveness has a profound effect on the total cost and optimal set of solutions. With as-bad-as-old restoration effectiveness, the optimal alternative involves maximizing the number of inspections without resorting to restoration. Conversely, with as-good-as-new restoration effectiveness, the optimal alternative should harmonize a moderate number of inspections with a moderate number of restorations. Additionally, the influence of the costs associated with restoration and accidents suggests that frequent inspections at smaller intervals are essential to mitigate the risk of failure. Maintenance managers can utilize the proposed model as a practical tool to optimize maintenance by comparing and selecting strategies based on effectiveness and cost parameters, thereby enhancing the reliability of repairable systems with hidden failures.

In this study, the risk-effectiveness framework proposed in NUREG/CR-5775 is developed based on the authors’ previously proposed Reliability, Availability, and Maintainability (RAM) model, which comprehensively considers all significant factors, including aging effects, test-caused degradation, test efficiency, and maintenance effectiveness. The developed RAM-based risk-effectiveness criterion, along with the Risk Management Technical Specification (RMTS) framework (RG 1.174 and 10 CFR 50.65(a)), is incorporated as a constraint in the optimization problem of surveillance test and maintenance (ST&M) planning for safety-related components in NPPs. As an application, the maintenance regime for a Motor-Operated Valve within the High-Pressure Injection System of a VVER/1000-V446 NPP, subjected to periodic tests and imperfect repairs, is optimized based on equipment RAM and its risk impact (ΔCDF) at the plant level. The results demonstrate that the most rational optimal ST&M planning must be determined as harmonizing and simultaneously positioning within the broad risk-effectiveness margin while also satisfying the criteria outlined in the RMTS framework. The proposed framework can provide valuable insights to maintenance decision-makers to evaluate and select appropriate maintenance strategies.

This paper proposes a safety-critical standby component unavailability model that contains aging effects caused by the elapsed time from installation, component degradation due to surveillance tests, and imperfect maintenance actions. An application of the model to a Motor-Operated Valve and a Motor-Driven Pump involved in the HPIS of a VVER/1000-V446 nuclear power plant is demonstrated and compared with other existing models at component and system levels. In addition, the effects of different unavailability models are reflected in the NPP's risk criterion, i.e., core damage frequency, over five maintenance periods. The results show that, compared with other models that do not simultaneously consider the full effects of degradation and maintenance impacts, the proposed model realistically evaluates the unavailabilities of the safety-related components and the involved systems as a plant age function. Therefore, it can effectively reflect the age-dependent CDF impact of a given testing and maintenance policy in a specified time horizon.

This research was devoted to optimising opportunistic tamping scheduling to present a cost-effective approach that considers both preventive and corrective tamping activities. To achieve this, we formulated the track geometry tamping scheduling problem as a mixed integer linear programming model and employed a genetic algorithm for its resolution. Key track quality indicators, including the standard deviation of the longitudinal level and single defects, were considered.We developed predictive models for the evolution of standard deviation and single defects over time, which were utilised to schedule preventive tamping activities and anticipate potential corrective actions. Additionally, we investigated the impact of both preventive and corrective tamping activities on the values of standard deviation and single defects.A case study on data from the Main Western Line in Sweden demonstrated that the fixed cost of occupying each maintenance window significantly influenced the total tamping cost. Moreover, the maintenance cycle interval notably affected the number of required corrective tamping activities. Specifically, a 3-month interval led to over 50 % fewer corrective tamping activities when compared to a 9-month interval. The results revealed that a 6-month interval achieved a favourable balance between corrective and preventive tamping activities and the total cost in our case study.

This study aimed to develop a multi-objective approach for reducing the positional errors in geometry measurements of track as a linear asset. Accordingly, we evaluated and compared two alignment methods – recursive segment-wise peak alignment (RSPA) and modified correlation optimised warping (MCOW). Furthermore, a novel rule-based approach was introduced to avoid data loss while aligning the datasets of the measurements of linear assets. A case study was conducted to implement and assess the performance of these methods in reducing the positional errors in track geometry measurements. The results revealed that the rule-based method preserves all the single defects present in the datasets. Furthermore, RSPA outperforms MCOW when aligning peaks, whereas MCOW is more efficient when all the data points in the datasets have equal priority.

Railway track, as a critical infrastructure, plays a significant role in freight transportation. However, Railway track degrades with age and usage and can impact negatively track availability and safety. Tamping actions are used to rejuvenate the degradation and recover the functionality of the track to an acceptable level. Tamping actions are performed in a form of preventive and corrective regimes. In performing an effective tamping regime, the recovery of both preventive and corrective tamping should be taken into account. In addition, the occurrence of isolated defects should be considered. By combining the recovery model with the degradation model, the long-term behavior of the track geometry can be predicted, and an accurate estimation of tamping needs can be provided, leading to optimum tamping scheduling. In this study, the effects of tamping recovery are modeled for both preventive and corrective strategies. For this aim, the values of both standard deviation (SD) and isolated defects have been predicted and their values before tamping are used as explanatory variables in a multivariable regression model. Finally, the effect of tamping recovery on the values of both SD and isolated defects is estimated. A case study is performed on a heavy haul line located in Sweden’s rail network to evaluate the performance of the proposed multivariable regression model. Observations showed that the model and its coefficients are significant with P-values close to zero, and the R-squared value suggests that the model explains approximately 70% of the variability in the response variable recovery.