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The present study proposes an economic indicator to support the evaluation of aircraft End of Life (EoL) strategies in view of the increasing demand with regards to aircraft decommissioning. This indicator can be used to evaluate the economic performance and to facilitate the trade-off studies among different strategies. First, Disposal and Recycle (D&R) scenarios related to stakeholders are investigated to identify the core concepts for the economic evaluation. Next, we extracted the aircraft D&R process from various real-life practices. In order to obtain the economic measure for the engineering process, a method of estimating the D&R cost and values are developed by integrating product, process and cost properties. This analysis is demonstrated on an averaged data set and two EoL aircraft cases. In addition, sensitivity analysis is performed to evaluate the impact of the D&R cost, residual value, and salvage value. Results show that the disassembly and dismantling of an aircraft engine possesses relatively more economic gains than that for the aircraft. The main factors influencing the proposed D&R economic indicator are the salvage value and D&R cost for economically efficient D&R cases. In addition, delaying the disposal and recycle process for EoL aircraft can lead to economically unfavorable solutions. The economic indicator combined with the evaluation methods is widely applicable for evaluations of engineering products EoL solutions, and implies a significant contribution of this research to decision making for such complex systems in terms sustainable policy. [ABSTRACT FROM AUTHOR]

This paper addresses a support information system for the planning of aircraft maintenance teams, assisting maintenance managers in delivering an aircraft on time. The developed planning of aircraft maintenance teams is a computer application based on a mathematical programming problem written as a minimization one. The initial decision variables are positive integer variables specifying the allocation of available technicians by skills to maintenance teams. The objective function is a nonlinear function balancing the time spent and costs incurred with aircraft fleet maintenance. The data involve technicians' skills, hours of work to perform maintenance tasks, costs related to facilities, and the aircraft downtime cost. The realism of this planning entails random possibilities associated with maintenance workload data, and the inference by a procedure of Monte Carlo simulation provides a proper set of workloads, instead of going through all the possibilities. The based formalization is a nonlinear integer programming problem, converted into an equivalent pure linear integer programming problem, using a transformation from initial positive integer variables to Boolean ones. A case study addresses the use of this support information system to plan a team for aircraft maintenance of three lines under the uncertainty of workloads, and a discussion of results shows the serviceableness of the proposed support information system. [ABSTRACT FROM AUTHOR]

In this research article, a novel endoscopic system, which is suited to perform a digital inspection of the aircraft wing fuel tanks, is introduced. The aim of this work is to specifically design and develop an assisting system, called 'Eeloscope', to allow accessing and diving through an aircraft kerosene tank in a minimally invasive matter. Currently, mechanics often suffer from the harsh working environment and the arduous maintenance duties within the tank. To address such challenges and derive a tailored solution, an adapted Design Thinking (DT) process is applied. The resulting system enables a fully digital inspection and generation of 3-dimensional structural inspection data. Consequently, devices such as the Eeloscope will facilitate a more efficient and continuous inspection of fuel tanks to increase the transparency regarding the condition of hardly accessible aircraft structures and provide a work relief for mechanics at the same time. [ABSTRACT FROM AUTHOR]

Flexible corrugated skins are ideal structures for morphing wings, and the associated load measurements are of great significance in structural health monitoring. This paper proposes a novel load-identification method for flexible corrugated skins based on improved Fisher discrimination dictionary learning (FDDL). Several fiber Bragg grating sensors are pasted on the skin to monitor the load on multiple corrugated crests. The loads on different crests cause nonuniform strain fields, and these discriminative spectra are recorded and used as training data. The proposed method involves load-positioning and load-size identification. In the load-size-identification stage, a classifier is trained for every corrugated crest. An interleaved block grouping of samples is introduced to enhance the discrimination of dictionaries, and a two-resolution load-size classifier is introduced to improve the performance and resolution of the grouping labels. An adjustable weight is introduced to the FDDL classification scheme to optimize the contribution from different sensors for different load-size classifiers. With the proposed method, the individual loads on eight crests can be identified by two fiber Bragg grating sensors. The positioning accuracy is 100%, and the mean error of the load-size identification is 0.2106 N, which is sufficiently precise for structural health monitoring. [ABSTRACT FROM AUTHOR]