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Aerospace
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Fault Detection and Prognostics in Aerospace Engineering II
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Fault Detection and Prognostics in Aerospace Engineering II

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

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 6386

Special Issue Editors

Dr. Matteo Davide Lorenzo Dalla Vedova

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering (DIMEAS), Politecnico di Torino, 10129 Turin, Italy
Interests: aerospace actuators; robots; applied mechanics; modeling and simulation; diagnostics; engineering; flap/slat actuation systems; FBG sensors; flight control systems; hydraulics; matlab simulink; mechatronics; on-board systems; prognostics; systems engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Pier Carlo Berri

E-Mail Website
Guest Editor
Department of Aerospace and Mechanical Engineering (DIMEAS), Politecnico di Torino, 10129 Turin, Italy
Interests: aerospace systems; diagnostic; electro-mechanical actuation systems; FBG-based sensors; minimally intrusive sensors for aerospace applications; model-based approach diagnostics; prognostics and diagnostics of aerospace systems
Special Issues, Collections and Topics in MDPI journals
Dr. Gaetano Quattrocchi

E-Mail Website
Guest Editor
Department of Aerospace and Mechanical Engineering (DIMEAS), Politecnico di Torino, 10129 Turin, Italy
Interests: aerospace systems; prognostics and diagnostics of aerospace systems; electro-mechanical actuation systems; FBG-based sensors; model-based approach prognostics; neural networks.

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to the second volume of the Special Issue entitled “Fault Detection and Prognostics in Aerospace Engineering”, which focuses on prognostics, diagnostics, and innovative approaches to fault detection/identification in all sectors of aerospace engineering.

Effective and reliable diagnostic strategies, able to timely identify the incoming failures and neutralize or, at least, mitigate their effects, are essential in aerospace to guarantee a proper fulfillment of safety requirements. These methods are evolving in parallel with the increase in complexity and criticality of onboard systems and, especially in recent decades, have become a fundamental topic that defines the goodness of aerospace projects.

In this regard, in recent years, a new engineering discipline, called Prognostics and Health Management (PHM), has been developed as an innovative strategy to reduce risks associated with the propagation of progressive failures. Briefly, PHM relies on monitoring a system's functional parameters to detect and identify precursors of failures at an early stage to estimate the components Remaining Useful Life (RUL). Furthermore, the information regarding the system's health status may help to better plan maintenance operations and warehouse management. As a result, most necessary maintenance interventions can be scheduled ahead of time instead of being performed as corrective maintenance. Furthermore, the aircraft operating profile could be adapted to slow down the evolution of failures, thereby increasing its availability and lowering operating costs. Finally, a reliable prognostic strategy supporting the aircraft maintenance activity would lead to more straightforward troubleshooting tasks, reducing the vehicle downtime and mitigating the risks associated with the human factor in fault identification.

These are considered cutting-edge topics in the scientific community and attract growing interest in various industrial sectors (aerospace, automotive, automation, railways, defense, and more). Therefore, we believe that a collection of selected works providing an overview of state-of-the-art research and highlighting the most recent and promising studies could be received with interest by the technical–scientific community.

To provide a thematic focus between the different application areas, this Special Issue aims to collect original research on innovative methods to address system engineering problems such as:

  • Aerospace actuators;
  • Aircraft flight control system;
  • Complex aerospace systems;
  • Diagnostics;
  • Dynamic simulation of the on-board system;
  • Fault detection/evaluation methods;
  • Mechatronics;
  • Model-based approach diagnostics;
  • Modelling techniques;
  • Monitoring systems;
  • Multi-domain numerical models;
  • Nonlinearities;
  • Numerical simulation;
  • Onboard systems;
  • PHM;
  • Prognostics;
  • Progressive failures;
  • Safety;
  • Simplified numerical models;
  • Systems design/optimization;
  • Systems engineering.

Furthermore, the key topics listed above are not intended to exclude articles from related areas. Indeed, we do not want to limit the Special Issue’s focus to only diagnostic and prognostic problems. Instead, we aim to also include significant studies concerning the analysis of the main failure modes affecting aerospace systems, their impact on operations, and the innovative techniques that simulate their effects.

We look forward to receiving your submissions and kindly invite you to contact one of the Guest Editors for further questions.

Dr. Matteo Davide Lorenzo Dalla Vedova
Dr. Pier Carlo Berri
Dr. Gaetano Quattrocchi
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.

Related Special Issue

Published Papers (3 papers)

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Research

21 pages, 5034 KiB  
Article
A Self-Supervised Fault Detection for UAV Based on Unbalanced Flight Data Representation Learning and Wavelet Analysis
by Shenghan Zhou, Tianhuai Wang, Linchao Yang, Zhao He and Siting Cao
Aerospace 2023, 10(3), 250; https://doi.org/10.3390/aerospace10030250 - 06 Mar 2023
Viewed by 1447
Abstract
This paper aims to build a Self-supervised Fault Detection Model for UAVs combined with an Auto-Encoder. With the development of data science, it is imperative to detect UAV faults and improve their safety. Many factors affect the fault of a UAV, such as [...] Read more.
This paper aims to build a Self-supervised Fault Detection Model for UAVs combined with an Auto-Encoder. With the development of data science, it is imperative to detect UAV faults and improve their safety. Many factors affect the fault of a UAV, such as the voltage of the generator, angle of attack, and position of the rudder surface. A UAV is a typical complex system, and its flight data are typical high-dimensional large sample data sets. In practical applications such as UAV fault detection, the fault data only appear in a small part of the data sets. In this study, representation learning is used to extract the normal features of the flight data and reduce the dimensions of the data. The normal data are used for the training of the Auto-Encoder, and the reconstruction loss is used as the criterion for fault detection. An Improved Auto-Encoder suitable for UAV Flight Data Sets is proposed in this paper. In the Auto-Encoder, we use wavelet analysis to extract the low-frequency signals with different frequencies from the flight data. The Auto-Encoder is used for the feature extraction and reconstruction of the low-frequency signals with different frequencies. To improve the effectiveness of the fault localization at inference, we develop a new fault factor location model, which is based on the reconstruction loss of the Auto-Encoder and edge detection operator. The UAV Flight Data Sets are used for hard-landing detection, and an average accuracy of 91.01% is obtained. Compared with other models, the results suggest that the developed Self-supervised Fault Detection Model for UAVs has better accuracy. Concluding this study, an explanation is provided concerning the proposed model’s good results. Full article
(This article belongs to the Special Issue Fault Detection and Prognostics in Aerospace Engineering II)
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23 pages, 1414 KiB  
Article
Uncertainty Quantification of Imperfect Diagnostics
by Vladimir Ulansky and Ahmed Raza
Aerospace 2023, 10(3), 233; https://doi.org/10.3390/aerospace10030233 - 27 Feb 2023
Cited by 1 | Viewed by 1885
Abstract
The operable state of a system is maintained during operation, which requires knowledge of the system’s state. Technical diagnostics, as a process of accurately obtaining information about the system state, becomes a crucial stage in the life cycle of any system. The study [...] Read more.
The operable state of a system is maintained during operation, which requires knowledge of the system’s state. Technical diagnostics, as a process of accurately obtaining information about the system state, becomes a crucial stage in the life cycle of any system. The study deals with the relevant problem of uncertainty quantification of imperfect diagnostics. We considered the most general case when the object of diagnostics, the diagnostic tool, and the human operator can each be in one of the many states. The concept of a diagnostic error is introduced, in which the object of diagnostics is in one of many states but is erroneously identified as being in any other state. We derived the generalized formulas for the probability of a diagnostic error, the probability of correct diagnosis, and the total probability of a diagnostic error. The proposed generalized formulas make it possible to determine the probabilistic indicators of diagnosis uncertainty for any structures of diagnostics systems and any types of failures of the diagnostic tool and human operator. We demonstrated the theoretical material by computing the probabilistic indicators of diagnosis uncertainty for an aircraft VHF communication system and fatigue cracks in the aircraft wings. Full article
(This article belongs to the Special Issue Fault Detection and Prognostics in Aerospace Engineering II)
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26 pages, 6382 KiB  
Article
A Fault Diagnosis Method under Data Imbalance Based on Generative Adversarial Network and Long Short-Term Memory Algorithms for Aircraft Hydraulic System
by Kenan Shen and Dongbiao Zhao
Aerospace 2023, 10(2), 164; https://doi.org/10.3390/aerospace10020164 - 10 Feb 2023
Cited by 2 | Viewed by 1701
Abstract
Safe and stable operation of the aircraft hydraulic system is of great significance to the flight safety of an aircraft. Any fault may be a threat to flight safety and may lead to enormous economic losses and even human casualties. Hence, the normal [...] Read more.
Safe and stable operation of the aircraft hydraulic system is of great significance to the flight safety of an aircraft. Any fault may be a threat to flight safety and may lead to enormous economic losses and even human casualties. Hence, the normal status of the aircraft hydraulic system is large, but very few data samples relate to the fault status. This causes a data imbalance in the fault diagnosis of the aircraft hydraulic system, which directly affects the accuracy of aircraft fault diagnosis. To solve the data imbalance problem in the fault diagnosis of the aircraft hydraulic system, this paper proposes an improved GAN-LSTM algorithm by using the improved GAN method, which can stably and accurately generate high-quality simulated fault samples using a small number of fault data. First, the model of the aircraft hydraulic system was built using AMESim software, and the imbalanced fault data and normal status data were acquired. Then, the imbalanced data were used to train the GAN model until the system reached a Nash equilibrium. By comparing the time domain and frequency signal, it was found that the quality of the generated sample was highly similar to the real sample. Moreover, LSTM (long short-term memory) and some other data-driven intelligent fault diagnosis methods were used as classifiers. The accuracy of these fault diagnosis methods increased steadily when the number of fault samples was gradually increased until it reached a balance with the normal sample. Meanwhile, three different sample generation methods were compared and analyzed to find the method with the best data generation ability. Finally, the anti-noise performance of the LSTM-GAN method was analyzed; this model has superior noise immunity. Full article
(This article belongs to the Special Issue Fault Detection and Prognostics in Aerospace Engineering II)
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