This paper proposes an analytical model that uses historical damage dimension data to deduce physical impactor characteristics (size and energy) that has caused a certain resulting damage. Maintenance tasks occur in operations due to impact, however the source of the damage caused in the event remains in most cases unknown. Consequently, by inferring what has caused a certain type of damage from the distribution of the damage type and severity relative to impactor types, maintainers can be better prepared in terms of what to expect from a given impactor source. The developed model introduces a novel transition deformation region between the local deformation and the global plate deflection, allowing for fast and accurate predictions of the impact event. Using the known aluminium structural properties and damage dimensions, the damage data is converted into impactor data. The model is applied in a case study using 120 fuselage dent damages dimensions (length, width, and depth) from a Boeing 777 fleet. The results show that the model deduces impactor characteristics for 94% of the considered damages, ranging up to 240 J and 110 mm for impactor energy and radius respectively.

Airline operators face accidental damages on their fleet of aircraft as part of operational practice. Individual occurrences are hard to predict; consequently, the approach towards repairing accidental damage is reactive in aircraft maintenance practice. However, by aggregating occurrence data and predicting future occurrence rates, it is possible to predict future long-term (strategic) demand for maintenance capacity. In this paper, a novel approach for integration of reliability modelling and inventory control is presented. Here, the concept of a base stock policy has been translated to the maintenance slot capacity problem to determine long-term cost-optimal capacity. Demand has been modelled using a superposed Non-homogeneous Poisson Process (NHPP). A case study has been performed on damage data from a fleet of Boeing 777 aircraft. The results prove the feasibility of adopting an integrated approach towards strategic capacity identification, using real-life data to predict future damage occurrence and associated maintenance slot requirements.

This paper proposes an approach towards multi-criteria decision making (MCDM) for operational maintenance processes. It focuses on decision alternative identification and evaluation for short time horizons, thereby addressing problems that need to be resolved in hours or a few days at maximum. This addresses a gap in literature, where MCDM methods are predominantly proposed for strategic maintenance decision making. The proposed approach addresses two distinct steps of decision making: 1) identification of decision alternatives and 2) evaluation of decision alternatives. For identification of decision options, the Boolean Decision Tree (BDT) method is selected to accommodate for the qualitative and discrete operational factors that determine the available, feasible decision alternatives in operational maintenance processes. The feasible alternatives are subsequently evaluated using the weighted sum method (WSM). The approach is applied to a Boeing 777 outboard flap damage case, using real maintenance and operational data. A decision tool has been developed and verified, showing the capability of the approach to systematically identify and evaluate operational maintenance decision making problems in a few minutes. The results suggest that the proposed approach could save in excess of 50% on decision process time, with added benefits in full identification of the available set of decision alternatives at problem onset. In addition, sensitivity analysis on the basis of a global evaluation of the weight space is provided to investigate the impact of weight settings on the decision outcomes.