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Aerospace
Special Issues
Aircraft Structural Health Monitoring and Digital Twin
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Aircraft Structural Health Monitoring and Digital Twin

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

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 2617

Special Issue Editors

Prof. Dr. Xiaojun Wang

E-Mail Website
Guest Editor
Institute of Solid Mechanics, School of Aeronautic Science and Engineering, Beihang University, Beijing 100083, China
Interests: structural reliability; multidisciplinary design optimization; structural health monitoring and digital twin of aircraft
Dr. Lei Wang

E-Mail Website
Guest Editor
Institute of Solid Mechanics, School of Aeronautic Science and Engineering, Beihang University, Beijing 100083, China
Interests: computational solid mechanics; aircraft structural dynamics; structural reliability; structural topology optimization; dynamic load identification for aircraft structures; structural damage identification and health monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The continuous advancement of aircraft technology has brought significant benefits to both production and daily life. However, complex operational conditions such as high speed, high altitude, and strong interference have imposed more demanding requirements on the utilization and maintenance of aircraft structures. This has led to greater safety-related and economic challenges.

To meet the evolving and intricate needs of aircraft structures in terms of operation, maintenance, and lifespan assessment, the fields of health monitoring and digital twin technology for aircraft structures have rapidly progressed and garnered widespread research attention. Structural health monitoring involves the continuous acquisition of pertinent information regarding the safety status of a structure in real-time. This information is crucial for providing early warnings of potential failures.

Digital twin technology has further elevated expectations regarding the precision and swiftness of structural health monitoring. It accomplishes this by creating a real-time, interactive digital representation of an aircraft structure's internal state, external environment, and future behavior. This is achieved through the integration of physical principles and intelligent algorithms.

However, due to various inherent challenges such as noise interference during data collection, uncertainty in analytical models, difficulties in parameter inversion, and the slow evolution of complex models, establishing efficient and accurate health monitoring and digital twin systems that can be trusted for aerospace structures is often a daunting task.

To address these challenges and pioneer breakthroughs in technology, this Special Edition warmly invites contributions focused on issues related to structural health monitoring and digital twin for aerospace structures. Topics of interest encompass, but are not limited to, structural health monitoring, digital twin technology, structural load identification, structural damage identification, the optimization of sensor placement, processing noisy data, the quantification of model uncertainties, the reconstruction of field variables, the identification of structural parameters, updates to and the evolution of twin models, structural strength evaluation, and lifespan prediction.

Prof. Dr. Xiaojun Wang
Dr. Lei Wang
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

  • aircraft structure
  • structural health monitoring
  • digital twin
  • structural loading identification
  • structural damage identification
  • sensor layout optimization
  • real-time monitoring
  • predictive maintenance

Published Papers (2 papers)

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Research

19 pages, 16336 KiB  
Article
Advancing the Diagnosis of Aero-Engine Bearing Faults with Rotational Spectrum and Scale-Aware Robust Network
by Jin Li, Zhengbing Yang, Xiang Zhou, Chenchen Song and Yafeng Wu
Aerospace 2024, 11(8), 613; https://doi.org/10.3390/aerospace11080613 - 26 Jul 2024
Abstract
The precise monitoring of bearings is crucial for the timely detection of issues in rotating mechanical systems. However, the high complexity of the structures makes the paths of vibration signal transmission exceedingly intricate, posing significant challenges in diagnosing aero-engine bearing faults. Therefore, a [...] Read more.
The precise monitoring of bearings is crucial for the timely detection of issues in rotating mechanical systems. However, the high complexity of the structures makes the paths of vibration signal transmission exceedingly intricate, posing significant challenges in diagnosing aero-engine bearing faults. Therefore, a Rotational-Spectrum-informed Scale-aware Robustness (RSSR) neural network is proposed in this study to address intricate fault characteristics and significant noise interference. The RSSR algorithm amalgamates a scale-aware feature extraction block, a non-activation convolutional network, and an innovative channel attention block, striking a balance between simplicity and efficacy. We provide a comprehensive analysis by comparing traditional CNNs, transformers, and their respective variants. Our strategy not only elevates diagnostic precision but also judiciously moderates the network’s parameter count and computational intensity, mitigating the propensity for overfitting. To assess the efficacy of our proposed network, we performed rigorous testing using two complex, publicly available datasets, with additional artificial noise introductions to simulate challenging operational environments. On the noise-free dataset, our technique increased the accuracy by 5.11% on the aero-engine dataset compared with the current mainstream methods. Even under maximal noise conditions, it enhances the average accuracy by 4.49% compared with other contemporary approaches. The results demonstrate that our approach outperforms other techniques in terms of diagnostic performance and generalization ability. Full article
(This article belongs to the Special Issue Aircraft Structural Health Monitoring and Digital Twin)
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29 pages, 2631 KiB  
Article
Preliminary Nose Landing Gear Digital Twin for Damage Detection
by Lucio Pinello, Omar Hassan, Marco Giglio and Claudio Sbarufatti
Aerospace 2024, 11(3), 222; https://doi.org/10.3390/aerospace11030222 - 12 Mar 2024
Cited by 1 | Viewed by 1187
Abstract
An increase in aircraft availability and readiness is one of the most desired characteristics of aircraft fleets. Unforeseen failures cause additional expenses and are particularly critical when thinking about combat jets and Unmanned Aerial Vehicles (UAVs). For instance, these systems are used under [...] Read more.
An increase in aircraft availability and readiness is one of the most desired characteristics of aircraft fleets. Unforeseen failures cause additional expenses and are particularly critical when thinking about combat jets and Unmanned Aerial Vehicles (UAVs). For instance, these systems are used under extreme conditions, and there can be situations where standard maintenance procedures are impractical or unfeasible. Thus, it is important to develop a Health and Usage Monitoring System (HUMS) that relies on diagnostic and prognostic algorithms to minimise maintenance downtime, improve safety and availability, and reduce maintenance costs. In particular, within the realm of aircraft structures, landing gear emerges as one of the most intricate systems, comprising several elements, such as actuators, shock absorbers, and structural components. Therefore, this work aims to develop a preliminary digital twin of a nose landing gear and implement diagnostic algorithms within the framework of the Health and Usage Monitoring System (HUMS). In this context, a digital twin can be used to build a database of signals acquired under healthy and faulty conditions on which damage detection algorithms can be implemented and tested. In particular, two algorithms have been implemented: the first is based on the Root-Mean-Square Error (RMSE), while the second relies on the Mahalanobis distance (MD). The algorithms were tested for three nose landing gear subsystems, namely, the steering system, the retraction/extraction system, and the oleo-pneumatic shock absorber. A comparison is made between the two algorithms using the ROC curve and accuracy, assuming equal weight for missed detections and false alarms. The algorithm that uses the Mahalanobis distance demonstrated superior performance, with a lower false alarm rate and higher accuracy compared to the other algorithm. Full article
(This article belongs to the Special Issue Aircraft Structural Health Monitoring and Digital Twin)
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