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Digitalization and Decision Support in Aerospace Maintenance Applications

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A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Aeronautics".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 65702

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Dr. Wim J. C. Verhagen

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Guest Editor

Department of Aerospace Engineering and Aviation, School of Engineering, RMIT University, Building 57, Level 3, 115 Queensberry St., Carlton, VIC 3053, Australia
Interests: aircraft maintenance; predictive maintenance; decision support
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Prof. Dr. Theodoros H. Loutas

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Co-Guest Editor

Applied Mechanics Laboratory, Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Rio, Greece
Interests: composite materials; structural health monitoring; intelligent algorithms for diagnostics/prognostics; fracture mechanics; experimental mechanics; fatigue
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Dr. Bruno F. Santos

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Co-Guest Editor

Faculty of Aerospace Engineering, Air Transport and Operations, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, The Netherlands
Interests: network development; fleet composition planning; robust and flexible operations; air cargo operations
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Special Issue Information

Dear colleagues,

The present Special Issue entitled “Digitalization and Decision Support in Aerospace Maintenance Applications” focuses on topics related to digitalization of aerospace maintenance processes, increased predictive capabilities enabled by these digitalized processes, and subsequent uptake of these capabilities in operational, tactical, and strategic decision support in maintenance applications.

Recent decades have seen significant advances in capabilities towards fault detection, diagnostics, and prognostics for aerospace structures and systems, as embodied in closely related engineering disciplines such as condition-based maintenance (CBM), prognostics and health management (PHM), integrated vehicle health management (IVHM), and predictive maintenance. These disciplines leverage the increased availability of data, including sensor data derived from (near-)continuous monitoring of systems and structures. In addition, the increase of operational and maintenance data derived from digitalization efforts in the aerospace sector has opened the door to broader development and adoption of data-driven models and methods. Nevertheless, significant challenges remain, including a relative paucity of event data, issues regarding data ownership and security, legislative requirements and constraints, operational acceptance and uptake of predictive models and tools, and a relative lack of methods and standards for consistent evaluation of the impact of predictive models and subsequent decision support throughout operations, support, and logistics.

Given that rapid advances are made within the scientific community in these areas, this Special Issue invites authors to submit full research articles or review manuscripts addressing (but not limited to) the following topics:

Digitalization efforts for aerospace maintenance applications, including models and frameworks for e-maintenance;
Predictive models for aerospace maintenance applications, including models for fault detection, diagnostics, and prognostics;
Decision support for operational, tactical, and strategic decision-making problems in the aerospace maintenance domain, including problems relevant to planning, scheduling, and/or execution phases;
Evaluation frameworks, methods, and models for predictive maintenance and decision support applications in aerospace maintenance;
Logistic implications of the adoption of CBM/PHM, including innovative spare parts forecasting and inventory management and optimization methods;
Legislative considerations regarding the future of digitalization and CBM/PHM in aerospace maintenance.

The focal topics listed above are not meant to exclude articles from additional related areas. We look forward to receiving your submissions and kindly invite you to address the Guest Editors in case of further questions.

Dr. Wim J.C. Verhagen
Dr. Bruno F. Santos
Prof. Dr. Theodoros H. Loutas
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Aerospace is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

digitalization
decision support
aerospace maintenance
condition-based maintenance (CBM)
prognostics and health management (PHM)
predictive maintenance

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Published Papers (9 papers)

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Research

34 pages, 3847 KiB

Open AccessArticle

A Modular Framework for the Life Cycle Based Evaluation of Aircraft Technologies, Maintenance Strategies, and Operational Decision Making Using Discrete Event Simulation

by Ahmad Ali Pohya, Jennifer Wehrspohn, Robert Meissner and Kai Wicke

Aerospace 2021, 8(7), 187; https://doi.org/10.3390/aerospace8070187 - 14 Jul 2021

Cited by 17 | Viewed by 4624

Abstract

Current practices for investment and technology decision making in aeronautics largely rely on regression-based cost estimation methods. Although quick to implement and easy to use, they suffer from a variety of limitations, both in temporal space and scope of applicability. While recent research and development in this area addresses these to a certain extent, aerospace engineering still lacks a flexible and customizable valuation framework. To this end, a generic environment for economic and operational assessment of aircraft and related products named LYFE is presented. This tool employs a discrete event simulation which models the product life cycle from its order through decades of operation and maintenance until disposal. This paper introduces its key characteristics and default methods alongside its modular program architecture. The capabilities are demonstrated with a case study of on-wing engine cleaning procedures which are triggered by a customized decision making module. Thereby, the impact on engine health, fuel efficiency and overall economic viability is quantified. On the whole, the framework introduced in this paper can be used to analyze not only physical products but also operational procedures and maintenance strategies as well as specified decision making algorithms in terms of their impact on an aircraft’s or system’s life cycle. Full article

(This article belongs to the Special Issue Digitalization and Decision Support in Aerospace Maintenance Applications)

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19 pages, 1884 KiB

Open AccessArticle

A Web-Based Decision Support System for Aircraft Dispatch and Maintenance

by Hemmo Koornneef, Wim J. C. Verhagen and Richard Curran

Aerospace 2021, 8(6), 154; https://doi.org/10.3390/aerospace8060154 - 28 May 2021

Cited by 3 | Viewed by 4320

Abstract

Aircraft dispatch involves determining the optimal dispatch option when an aircraft experiences an unexpected failure. Currently, maintenance technicians at the apron have limited access to support information and finding the right information in extensive maintenance manuals is a time-consuming task, often leading to technically induced delays. This paper introduces a novel web-based prototype decision support system to aid technicians during aircraft dispatch decision-making and subsequent maintenance execution. A system architecture for real-time dispatch decision support is established and implemented. The developed system is evaluated through a case study in an operational environment by licensed maintenance technicians. The system fully automates information retrieval from multiple data sources, performs alternative identification and evaluation for a given fault message, and provides the technician with on-site access to relevant information, including the related maintenance tasks. The case study indicates a potential time saving of up to 98% per dispatch decision. Moreover, it enables digitalization of the—currently mostly paper-based—dispatch decision process, thereby reducing logistics and paper waste. The prototype is the first to provide operational decision support in the aircraft maintenance domain and addresses the lack of correlation between theory and practice often found in decision support systems research by providing a representative case study. The developed custom parser for SGML-based documents enables efficient identification and extraction of relevant information, vastly contributing to the overall reduction of the decision time. Full article

(This article belongs to the Special Issue Digitalization and Decision Support in Aerospace Maintenance Applications)

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13 pages, 1834 KiB

Open AccessArticle

Planning of Aircraft Fleet Maintenance Teams

by Duarte P. Pereira, Isaias L. R. Gomes, Rui Melicio and Victor M. F. Mendes

Aerospace 2021, 8(5), 140; https://doi.org/10.3390/aerospace8050140 - 19 May 2021

Cited by 8 | Viewed by 3476

Abstract

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. Full article

(This article belongs to the Special Issue Digitalization and Decision Support in Aerospace Maintenance Applications)

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26 pages, 7342 KiB

Open AccessArticle

Eeloscope—Towards a Novel Endoscopic System Enabling Digital Aircraft Fuel Tank Maintenance

by Florian Heilemann, Alireza Dadashi and Kai Wicke

Aerospace 2021, 8(5), 136; https://doi.org/10.3390/aerospace8050136 - 12 May 2021

Cited by 8 | Viewed by 7902

Abstract

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. Full article

(This article belongs to the Special Issue Digitalization and Decision Support in Aerospace Maintenance Applications)

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19 pages, 7377 KiB

Open AccessArticle

Load-Identification Method for Flexible Multiple Corrugated Skin Using Spectra Features of FBGs

by Zhaoyu Zheng, Jiyun Lu and Dakai Liang

Aerospace 2021, 8(5), 134; https://doi.org/10.3390/aerospace8050134 - 9 May 2021

Cited by 2 | Viewed by 2624

Abstract

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. Full article

(This article belongs to the Special Issue Digitalization and Decision Support in Aerospace Maintenance Applications)

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30 pages, 17662 KiB

Open AccessArticle

Methodology for Evaluating Risk of Visual Inspection Tasks of Aircraft Engine Blades

by Jonas Aust and Dirk Pons

Aerospace 2021, 8(4), 117; https://doi.org/10.3390/aerospace8040117 - 19 Apr 2021

Cited by 13 | Viewed by 6030

Abstract

Risk assessment methods are widely used in aviation, but have not been demonstrated for visual inspection of aircraft engine components. The complexity in this field arises from the variety of defect types and the different manifestation thereof with each level of disassembly. A new risk framework was designed to include contextual factors. Those factors were identified using Bowtie analysis to be criticality, severity, and detectability. This framework yields a risk metric that describes the extent to which a defect might stay undetected during the inspection task, and result in adverse safety outcomes. A simplification of the framework provides a method for go/no-go decision-making. The results of the study reveal that the defect detectability is highly dependent on specific views of the blade, and the risk can be quantified. Defects that involve material separation or removal such as scratches, tip rub, nicks, tears, cracks, and breaking, are best shown in airfoil views. Defects that involve material deformation and change of shape, such as tip curl, dents on the leading edges, bents, and battered blades, have lower risk if edge views can be provided. This research proposes that many risk assessments may be reduced to three factors: consequence, likelihood, and a cofactor. The latter represents the industrial context, and can comprise multiple sub-factors that are application-specific. A method has been devised, including appropriate scales, for the inclusion of these into the risk assessment. Full article

(This article belongs to the Special Issue Digitalization and Decision Support in Aerospace Maintenance Applications)

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18 pages, 528 KiB

Open AccessArticle

Aircraft Maintenance Check Scheduling Using Reinforcement Learning

by Pedro Andrade, Catarina Silva, Bernardete Ribeiro and Bruno F. Santos

Aerospace 2021, 8(4), 113; https://doi.org/10.3390/aerospace8040113 - 17 Apr 2021

Cited by 25 | Viewed by 6306

Abstract

This paper presents a Reinforcement Learning (RL) approach to optimize the long-term scheduling of maintenance for an aircraft fleet. The problem considers fleet status, maintenance capacity, and other maintenance constraints to schedule hangar checks for a specified time horizon. The checks are scheduled within an interval, and the goal is to, schedule them as close as possible to their due date. In doing so, the number of checks is reduced, and the fleet availability increases. A Deep Q-learning algorithm is used to optimize the scheduling policy. The model is validated in a real scenario using maintenance data from 45 aircraft. The maintenance plan that is generated with our approach is compared with a previous study, which presented a Dynamic Programming (DP) based approach and airline estimations for the same period. The results show a reduction in the number of checks scheduled, which indicates the potential of RL in solving this problem. The adaptability of RL is also tested by introducing small disturbances in the initial conditions. After training the model with these simulated scenarios, the results show the robustness of the RL approach and its ability to generate efficient maintenance plans in only a few seconds. Full article

(This article belongs to the Special Issue Digitalization and Decision Support in Aerospace Maintenance Applications)

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33 pages, 3941 KiB

Open AccessArticle

Aircraft Fleet Health Monitoring with Anomaly Detection Techniques

by Luis Basora, Paloma Bry, Xavier Olive and Floris Freeman

Aerospace 2021, 8(4), 103; https://doi.org/10.3390/aerospace8040103 - 7 Apr 2021

Cited by 30 | Viewed by 15503

Abstract

Predictive maintenance has received considerable attention in the aviation industry where costs, system availability and reliability are major concerns. In spite of recent advances, effective health monitoring and prognostics for the scheduling of condition-based maintenance operations is still very challenging. The increasing availability of maintenance and operational data along with recent progress made in machine learning has boosted the development of data-driven prognostics and health management (PHM) models. In this paper, we describe the data workflow in place at an airline for the maintenance of an aircraft system and highlight the difficulties related to a proper labelling of the health status of such systems, resulting in a poor suitability of supervised learning techniques. We focus on investigating the feasibility and the potential of semi-supervised anomaly detection methods for the health monitoring of a real aircraft system. Proposed methods are evaluated on large volumes of real sensor data from a cooling unit system on a modern wide body aircraft from a major European airline. For the sake of confidentiality, data has been anonymized and only few technical and operational details about the system had been made available. We trained several deep neural network autoencoder architectures on nominal data and used the anomaly scores to calculate a health indicator. Results suggest that high anomaly scores are correlated with identified failures in the maintenance logs. Also, some situations see an increase in the anomaly score for several flights prior to the system’s failure, which paves a natural way for early fault identification. Full article

(This article belongs to the Special Issue Digitalization and Decision Support in Aerospace Maintenance Applications)

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27 pages, 8573 KiB

Open AccessArticle

Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection

by Jonas Aust, Sam Shankland, Dirk Pons, Ramakrishnan Mukundan and Antonija Mitrovic

Aerospace 2021, 8(2), 30; https://doi.org/10.3390/aerospace8020030 - 26 Jan 2021

Cited by 40 | Viewed by 12890

Abstract

Background—In the field of aviation, maintenance and inspections of engines are vitally important in ensuring the safe functionality of fault-free aircrafts. There is value in exploring automated defect detection systems that can assist in this process. Existing effort has mostly been directed at artificial intelligence, specifically neural networks. However, that approach is critically dependent on large datasets, which can be problematic to obtain. For more specialised cases where data are sparse, the image processing techniques have potential, but this is poorly represented in the literature. Aim—This research sought to develop methods (a) to automatically detect defects on the edges of engine blades (nicks, dents and tears) and (b) to support the decision-making of the inspector when providing a recommended maintenance action based on the engine manual. Findings—For a small sample test size of 60 blades, the combined system was able to detect and locate the defects with an accuracy of 83%. It quantified morphological features of defect size and location. False positive and false negative rates were 46% and 17% respectively based on ground truth. Originality—The work shows that image-processing approaches have potential value as a method for detecting defects in small data sets. The work also identifies which viewing perspectives are more favourable for automated detection, namely, those that are perpendicular to the blade surface. Full article

(This article belongs to the Special Issue Digitalization and Decision Support in Aerospace Maintenance Applications)

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