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Selected Papers from the 1st International Conference for CBM in...
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Selected Papers from the 1st International Conference for CBM in Aerospace

A special issue of Aerospace (ISSN 2226-4310).

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 23595

Special Issue Editors

Dr. Bruno F. Santos

E-Mail Website
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
Prof. Dr. Theodoros H. Loutas

E-Mail Website
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
Dr. Dimitrios Zarouchas

E-Mail Website
Guest Editor
Structural Integrity & Composites Group, Faculty of Aerospace Engineering, Delft University of Technology, 2624 Delft, HS, The Netherlands
Interests: AI for structures; prognostics; diagnostics; structural health monitoring; intelligent structures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is cooperating with the 1st International Conference for CBM in Aerospace, which will take place on the 24–25 May 2022 at Delft, the Netherlands.

The conference aims to create an international platform for academicians, researchers, managers, industrial participants, and students to share their research findings in reliability engineering, sensing technology, predictive algorithms, data science and IT, artificial intelligence for CBM, optimization solutions, digital-twin models, and safety and certification related to CBM. Authors of contributions relating to the above key themes are welcome to submit extended versions of their conference work to this Special Issue for publication in our journal Aerospace.

Dr. Bruno F. Santos
Prof. Dr. Theodoros H. Loutas
Dr. Dimitrios Zarouchas
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

  • prognostics and health management
  • structural health monitoring
  • reliability and safety
  • maintenance planning and practice

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Published Papers (7 papers)

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Research

14 pages, 1473 KiB  
Article
A Comparative Study of Optimization Models for Condition-Based Maintenance Scheduling of an Aircraft Fleet
by Iordanis Tseremoglou, Paul J. van Kessel and Bruno F. Santos
Aerospace 2023, 10(2), 120; https://doi.org/10.3390/aerospace10020120 - 27 Jan 2023
Cited by 6 | Viewed by 3267
Abstract
Condition-based maintenance (CBM) scheduling of an aircraft fleet in a disruptive environment while considering health prognostics for a set of systems is a very complex combinatorial problem, which is becoming more challenging in light of the uncertainty included in health prognostics. This type [...] Read more.
Condition-based maintenance (CBM) scheduling of an aircraft fleet in a disruptive environment while considering health prognostics for a set of systems is a very complex combinatorial problem, which is becoming more challenging in light of the uncertainty included in health prognostics. This type of problem falls under the broad category of resource-constrained scheduling problems under uncertainty and is often solved using a mixed integer linear programming (MILP) formulation. While a MILP framework is very promising, the problem size can scale exponentially with the number of considered aircraft and considered tasks, leading to significantly high computational costs. The most recent advances in artificial intelligence have demonstrated the capability of deep reinforcement learning (DRL) algorithms to alleviate this curse of dimensionality, as once the DRL agent is trained, it can achieve real-time optimization of the maintenance schedule. However, there is no guarantee of optimality. These comparative merits of a MILP and a DRL formulation for the aircraft fleet maintenance scheduling problem have not been discussed in the literature. This study is a response to this research gap. We conduct a comparison of a MILP and a DRL scheduling model, which are used to derive the optimal maintenance schedule for various maintenance scenarios for aircraft fleets of different sizes in a disruptive environment, while considering health prognostics and the available resources for the execution of each task. The quality of solutions is evaluated on the basis of four planning objectives, defined according to real airline practice. The results show that the DRL approach achieves better results with respect to scheduling of prognostics-driven tasks and requires less computational time, whereas the MILP model produces more stable maintenance schedules and induces less maintenance ground time. Overall, the comparison provides valuable insights for the integration of health prognostics in airline maintenance practice. Full article
(This article belongs to the Special Issue Selected Papers from the 1st International Conference for CBM in Aerospace)
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17 pages, 5346 KiB  
Article
Adapting Commercial Best Practices to U.S. Air Force Maintenance Scheduling
by Kyle Blond, Austin Himschoot, Eric Klein, Steven Conley and Anne Clark
Aerospace 2023, 10(1), 61; https://doi.org/10.3390/aerospace10010061 - 7 Jan 2023
Cited by 3 | Viewed by 3480
Abstract
This paper presents how the Inspection Development Framework’s (IDF) novel maintenance scheduling technique increased aircraft utilization and availability in a sample of the United States Air Force’s (USAF) C-5M Super Galaxy fleet. The hypothesis tested was “Can we execute segmented maintenance requirements during [...] Read more.
This paper presents how the Inspection Development Framework’s (IDF) novel maintenance scheduling technique increased aircraft utilization and availability in a sample of the United States Air Force’s (USAF) C-5M Super Galaxy fleet. The hypothesis tested was “Can we execute segmented maintenance requirements during ground time opportunities in order to optimize flying?” We applied IDF to decompose the C-5M’s five-day Home Station Check (HSC) inspection into smaller work packages that subordinate to operational requirements and maintenance resource availability. Ten HSCs at Dover and Travis Air Force Base (AFB) were modified using IDF and measured against a control group of traditional HSCs. While statistical significance was not achieved given the small sample size, anecdotal results demonstrate improvements in maintenance downtime, sortie count, and flight hours for the experimental group across the two bases. Specifically, the pathfinder’s observed results extrapolated to all HSCs at each base projected an additional 15 flying days per year at Dover AFB and 29 sorties per year at Travis AFB. These C-5M improvements serve as a proof-of-concept for the USAF adapting commercial best practices to address declining aircraft readiness. IDF’s more agile and dynamic scheduling techniques also enable easier adoption of Condition Based Maintenance through a more integrated approach to optimally schedule maintenance requirements. Full article
(This article belongs to the Special Issue Selected Papers from the 1st International Conference for CBM in Aerospace)
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19 pages, 935 KiB  
Article
Simulation Framework for Real-Time PHM Applications in a System-of-Systems Environment
by Lorenz Dingeldein
Aerospace 2023, 10(1), 58; https://doi.org/10.3390/aerospace10010058 - 6 Jan 2023
Cited by 3 | Viewed by 2970
Abstract
While the growth of unmanned aerial vehicle (UAV) usage over the next few years is indisputable, cooperative operation strategies for UAV swarms have gained great interest in the research community. Mission capabilities increase while contingencies can be mitigated through intelligent management between the [...] Read more.
While the growth of unmanned aerial vehicle (UAV) usage over the next few years is indisputable, cooperative operation strategies for UAV swarms have gained great interest in the research community. Mission capabilities increase while contingencies can be mitigated through intelligent management between the operating swarm and the available fleet. The importance of observing the system reliability and of risk assessment grows because the dysfunction of one asset within a system of systems endangers the superordinate mission goals of the operating UAV swarm. Thus, not only is trajectory planning beneficial for usage optimization, but prognostic and health management (PHM) methods, including diagnostics and prognostics, also enable situational awareness and condition-driven asset management to achieve higher mission reliability. The novelty of this work is the observation of asset states based upon a generically modeled multi-component degradation behavior and the integration of PHM methods with real-time capabilities in order to support decision making during mission execution in a highly dynamic and event-based environment. The developed simulation enables the testing and comparison of different maintenance strategies that are integrated into the simulation to show and discuss the effectiveness and benefits of real-time-capable PHM methods. Full article
(This article belongs to the Special Issue Selected Papers from the 1st International Conference for CBM in Aerospace)
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27 pages, 2891 KiB  
Article
A Generic Framework for Prognostics of Complex Systems
by Marie Bieber and Wim J. C. Verhagen
Aerospace 2022, 9(12), 839; https://doi.org/10.3390/aerospace9120839 - 16 Dec 2022
Cited by 2 | Viewed by 2059
Abstract
In recent years, there has been an enormous increase in the amount of research in the field of prognostics and predictive maintenance for mechanical and electrical systems. Most of the existing approaches are tailored to one specific system. They do not provide a [...] Read more.
In recent years, there has been an enormous increase in the amount of research in the field of prognostics and predictive maintenance for mechanical and electrical systems. Most of the existing approaches are tailored to one specific system. They do not provide a high degree of flexibility and often cannot be adaptively used on different systems. This can lead to years of research, knowledge, and expertise being put in the implementation of prognostics models without the capacity to estimate the remaining useful life of systems, either because of lack of data or data quality or simply because failure behaviour cannot be captured by data-driven models. To overcome this, in this paper we present an adaptive prognostic framework which can be applied to different systems while providing a way to assess whether or not it makes sense to put more time into the development of prognostic models for a system. The framework incorporates steps necessary for prognostics, including data pre-processing, feature extraction and machine learning algorithms for remaining useful life estimation. The framework is applied to two systems: a simulated turbofan engine dataset and an aircraft cooling unit dataset. The results show that the obtained accuracy of the remaining useful life estimates are comparable to what has been achieved in literature and highlight considerations for suitability assessment of systems data towards prognostics. Full article
(This article belongs to the Special Issue Selected Papers from the 1st International Conference for CBM in Aerospace)
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17 pages, 12129 KiB  
Article
Playful Probing: Towards Understanding the Interaction with Machine Learning in the Design of Maintenance Planning Tools
by Jorge Ribeiro and Licínio Roque
Aerospace 2022, 9(12), 754; https://doi.org/10.3390/aerospace9120754 - 26 Nov 2022
Cited by 1 | Viewed by 1733
Abstract
In the context of understanding interaction with artificial intelligence algorithms in a decision support system, this study addresses the use of a playful probe as a potential speculative design approach. We describe the process of researching a new machine learning (ML)-based planning tool [...] Read more.
In the context of understanding interaction with artificial intelligence algorithms in a decision support system, this study addresses the use of a playful probe as a potential speculative design approach. We describe the process of researching a new machine learning (ML)-based planning tool for maintenance based on aircraft conditions and the challenge of investigating how playful probes can enable end-user participation during the process of design. Using a design science research approach, we designed a playful probe protocol and materials and evaluated results by running a participatory design workshop. With this approach, participants facilitated speculative design insights into understandable interactions, especially with ML interaction. The article contributes with a design of a playful probe exercise to collaboratively study the adjustment of practices for CBM and a set of concrete insights on understandable interactions with CBM. Full article
(This article belongs to the Special Issue Selected Papers from the 1st International Conference for CBM in Aerospace)
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16 pages, 3667 KiB  
Article
AI-Based Exhaust Gas Temperature Prediction for Trustworthy Safety-Critical Applications
by Asteris Apostolidis, Nicolas Bouriquet and Konstantinos P. Stamoulis
Aerospace 2022, 9(11), 722; https://doi.org/10.3390/aerospace9110722 - 17 Nov 2022
Cited by 7 | Viewed by 3999
Abstract
Data-driven condition-based maintenance (CBM) and predictive maintenance (PdM) strategies have emerged over recent years and aim at minimizing the aviation maintenance costs and environmental impact by the diagnosis and prognosis of aircraft systems. As the use of data and relevant algorithms is essential [...] Read more.
Data-driven condition-based maintenance (CBM) and predictive maintenance (PdM) strategies have emerged over recent years and aim at minimizing the aviation maintenance costs and environmental impact by the diagnosis and prognosis of aircraft systems. As the use of data and relevant algorithms is essential to AI-based gas turbine diagnostics, there are different technical, operational, and regulatory challenges that need to be tackled in order for the aeronautical industry to be able to exploit their full potential. In this work, the machine learning (ML) method of the generalised additive model (GAM) is used in order to predict the evolution of an aero engine’s exhaust gas temperature (EGT). Three different continuous synthetic data sets developed by NASA are employed, known as New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS), with increasing complexity in engine deterioration. The results show that the GAM can be predict the evolution of the EGT with high accuracy when using several input features that resemble the types of physical sensors installed in aero gas turbines currently in operation. As the GAM offers good interpretability, this case study is used to discuss the different data attributes a data set needs to have in order to build trust and move towards certifiable models in the future. Full article
(This article belongs to the Special Issue Selected Papers from the 1st International Conference for CBM in Aerospace)
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17 pages, 5688 KiB  
Article
Usage Monitoring of Helicopter Gearboxes with ADS-B Flight Data
by David Hünemohr, Jörg Litzba and Farid Rahimi
Aerospace 2022, 9(11), 647; https://doi.org/10.3390/aerospace9110647 - 25 Oct 2022
Cited by 4 | Viewed by 3496
Abstract
Health and usage monitoring systems (HUMS) are the basis for condition-based maintenance of helicopters. One of the most critical systems in terms of safety and maintenance expense that can be monitored by HUMS are the main gearboxes of helicopters with turbine engines. While [...] Read more.
Health and usage monitoring systems (HUMS) are the basis for condition-based maintenance of helicopters. One of the most critical systems in terms of safety and maintenance expense that can be monitored by HUMS are the main gearboxes of helicopters with turbine engines. While the health monitoring part of HUMS aims to model the health state from the collected sensor data with advanced algorithms, such as machine learning, the usage monitoring part tracks the time of use and operating parameters of the system, such as load, to determine lifetime consumption. In the presented work, a combination of automatic dependent surveillance-broadcast (ADS-B) flight data with a generic helicopter performance model is used to acquire torque profiles of the gearboxes. With damage accumulation methods, the load spectra are transformed to aggregated indicators that reflect the individual gearbox usage. The methodology is applied to samples of two helicopters from a five-year ADS-B data set of German helicopter emergency medical services (HEMS) acquired for the study. The results demonstrate the feasibility of the generic approach, which can support maintenance scheduling and new usage-based maintenance services independent of direct access to installed HUMS. Full article
(This article belongs to the Special Issue Selected Papers from the 1st International Conference for CBM in Aerospace)
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