The purpose of this paper is to describe a methodology that supports an assessment of the operational consequences of failures in aircraft systems and its associated costs, in order to facilitate a correct and efficient decision-making during cost-effectiveness analysis of maintenance tasks within scheduled aircraft maintenance program development. The paper is based on empirical studies of possible scenarios from aircraft failure to operational consequences in commercial airlines. Empirical data was extracted through document studies and interviews, guided by the application of an event tree analysis (ETA). The analysis was performed together with experienced practitioners from both an aircraft manufacturer and commercial airlines, which contributed to a continuous verification of the outcomes of the study. The proposed methodology, which is based on ETA, is considered as a valuable support in the assessment of the operational consequences of failures within a MSG-3 framework. The proposed methodology focuses on assessing the operational consequences of failures and associated economical losses. Hence, in order to enable an estimation of the maintenance tasks' cost-effectiveness, the methodology should be further developed to include a cost assessment of the applicable maintenance tasks. The proposed methodology could be adapted as a support to those involved in the development of aircraft maintenance program. The operational consequences and the probabilities of the proposed event tree can be quantified by the aid of historical data or expert judgment. No other research paper or other available documentation dealing with the assessment of the cost of failures with operational consequences has been encountered by the authors. Hence, the proposed methodology fills this gap and provides a fulfillment of practitioners' needs.

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.