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Aerospace
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Aerospace Prognosis Technology
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Aerospace Prognosis Technology

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

Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 12306

Special Issue Editor

Dr. Marcia Lourenco Baptista

E-Mail Website
Guest Editor
Department of Aerospace Engineering, Aerospace Transport & Operations, Delft University of Technology, 2628 CD Delft, Netherlands
Interests: prognostics; predictive maintenance; data-driven methods; artificial intelligence; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, we have seen an increase in interest in the field of prognostics and health management (PHM). This can, to some extent, be explained by recent progress in predictive technologies that estimate the remaining useful life (RUL) of different equipment. This exercise, called prognostics, is of  importance to a range of industries, including aeronautics, naval, energy, and manufacturing. As a core technology of predictive maintenance, prognostics can provide unprecedent insights into the degradation processes and how to better schedule and plan future operations.

This Special Issue on Aerospace Prognosis Technology aims at collecting the newest research and developments trends in the field of aircraft prognostics technology, which may include:

  • The development of feature extraction methods to support prognostics methodologies;
  • The use of signal processing and denoising techniques to preprocess the data utilized in prognostics;
  • Model-based methods based on filtering techniques to prognose failure;
  • Data-driven techniques based on machine learning and neural networks;
  • Hybrid modeling to advance the integration of physics into neural networks and other data-driven models;
  • Explainability methods driven by artificial intelligence methods;
  • Evaluation techniques and metrics of prognostics outputs;
  • IoT and its connection to prognostics;
  • Digital twins and simulation to advance prognostics;
  • Scheduling and planning based on prognostics.

Dr. Marcia Lourenco Baptista
Guest Editor

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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

24 pages, 8372 KiB  
Article
Physics-Guided Neural Network Model for Aeroengine Control System Sensor Fault Diagnosis under Dynamic Conditions
by Huihui Li, Linfeng Gou, Huacong Li and Zhidan Liu
Aerospace 2023, 10(7), 644; https://doi.org/10.3390/aerospace10070644 - 18 Jul 2023
Cited by 8 | Viewed by 2520
Abstract
Sensor health assessments are of great importance for accurately understanding the health of an aeroengine, supporting maintenance decisions, and ensuring flight safety. This study proposes an intelligent framework based on a physically guided neural network (PGNN) and convolutional neural network (CNN) to diagnose [...] Read more.
Sensor health assessments are of great importance for accurately understanding the health of an aeroengine, supporting maintenance decisions, and ensuring flight safety. This study proposes an intelligent framework based on a physically guided neural network (PGNN) and convolutional neural network (CNN) to diagnose sensor faults under dynamic conditions. The strength of the approach is that it integrates information from physics-based performance models and deep learning models. In addition, it has the structure of prediction–residual–generation-fault classification that effectively decouples the interaction between sensor faults and system state changes. First, a PGNN generates the engine’s non-linear dynamic prediction output because the PGNN has the advantage of being able to handle temporal information from the long short-term memory (LSTM) network. We use a cross-physics–data fusion scheme as the prediction strategy to explore the hidden information of the physical model output and sensor measurement data. A novel loss function that considers physical discipline is also proposed to overcome the performance limitations of traditional data-driven models because of their physically inconsistent representations. Then, the predicted values of the PGNN are compared with the sensor measurements to obtain a residual signal. Finally, a convolutional neural network (CNN) is used to classify faults for residual signals and deliver diagnostic results. Furthermore, the feasibility of the proposed framework is demonstrated on an engine sensor fault dataset. The experimental results show that the proposed method outperforms the pure data-driven approach, with the predicted RMSE being reduced from 1.6731 to 0.9897 and the diagnostic accuracy reaching 95.9048%, thereby confirming its superior performance. Full article
(This article belongs to the Special Issue Aerospace Prognosis Technology)
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11 pages, 1636 KiB  
Article
A Smart Airport Mobile Application Concept and Possibilities of Its Use for Predictive Modeling and Analysis
by Martin Baláž, Kristína Kováčiková, Juraj Vaculík and Martina Kováčiková
Aerospace 2023, 10(7), 588; https://doi.org/10.3390/aerospace10070588 - 27 Jun 2023
Cited by 6 | Viewed by 2891
Abstract
The goal of this paper is to propose a smart airport solution, which is customer-oriented and suitable for an airport at the beginning of the process of digitization. Such a solution is represented by a mobile application, which allows the airport to provide [...] Read more.
The goal of this paper is to propose a smart airport solution, which is customer-oriented and suitable for an airport at the beginning of the process of digitization. Such a solution is represented by a mobile application, which allows the airport to provide its customers with basic information faster, more efficiently, in a simpler manner, and without the need for face-to-face interaction. The data collected through a smart airport mobile application can be used in conjunction with other technologies or systems for predictive modeling and analysis. The main benefit of the paper is the primary research aimed at the identification of customer requirements for a specific airport from the perspective of services and functions that the mobile application should offer. Subsequently, based on the analysis, a proposal for an airport application for mobile devices is developed through UX and UI design. The design consists of six successive phases and results in the development of an interactive prototype of the required mobile application. In addition, the paper discusses how the data collected through a smart airport mobile application can potentially be used in conjunction with other technologies or systems for predictive modeling and analysis. Full article
(This article belongs to the Special Issue Aerospace Prognosis Technology)
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23 pages, 1496 KiB  
Article
An Explainable Artificial Intelligence Approach for Remaining Useful Life Prediction
by Genane Youness and Adam Aalah
Aerospace 2023, 10(5), 474; https://doi.org/10.3390/aerospace10050474 - 18 May 2023
Cited by 9 | Viewed by 3366
Abstract
Prognosis and health management depend on sufficient prior knowledge of the degradation process of critical components to predict the remaining useful life. This task is composed of two phases: learning and prediction. The first phase uses the available information to learn the system’s [...] Read more.
Prognosis and health management depend on sufficient prior knowledge of the degradation process of critical components to predict the remaining useful life. This task is composed of two phases: learning and prediction. The first phase uses the available information to learn the system’s behavior. The second phase predicts future behavior based on the available information of the system and estimates its remaining lifetime. Deep learning approaches achieve good prognostic performance but usually suffer from a high computational load and a lack of interpretability. Complex feature extraction models do not solve this problem, as they lose information in the learning phase and thus have a poor prognosis for the remaining lifetime. A new prepossessing approach is used with feature clustering to address this issue. It allows for restructuring the data into homogeneous groups strongly related to each other using a simple architecture of the LSTM model. It is advantageous in terms of learning time and the possibility of using limited computational capabilities. Then, we focus on the interpretability of deep learning prognosis using Explainable AI to achieve interpretable RUL prediction. The proposed approach offers model improvement and enhanced interpretability, enabling a better understanding of feature contributions. Experimental results on the available NASA C-MAPSS dataset show the performance of the proposed model compared to other common methods. Full article
(This article belongs to the Special Issue Aerospace Prognosis Technology)
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24 pages, 1439 KiB  
Article
Aircraft Engine Bleed Valve Prognostics Using Multiclass Gated Recurrent Unit
by Marcia L. Baptista and Helmut Prendinger
Aerospace 2023, 10(4), 354; https://doi.org/10.3390/aerospace10040354 - 3 Apr 2023
Cited by 2 | Viewed by 2091
Abstract
Prognostics and health management is an engineering discipline that aims to support system operation while ensuring maximum safety and performance. Prognostics is a key step of this framework, focusing on developing effective maintenance policies based on predictive methods. Traditionally, prognostics models forecast the [...] Read more.
Prognostics and health management is an engineering discipline that aims to support system operation while ensuring maximum safety and performance. Prognostics is a key step of this framework, focusing on developing effective maintenance policies based on predictive methods. Traditionally, prognostics models forecast the degradation process using regression techniques that approximate a mapping function from input to continuous remaining useful life estimates. These models are typically of high complexity and low interpretability. Classification approaches are an alternative solution to these types of models. We propose a predictive classification model that translates the input into discrete output variables instead of mapping the input to a single remaining useful life estimate. Each discrete output variable corresponds to a range of remaining useful life values. In other words, each output class variable represents the likelihood or risk of failure within a specific time range. We apply this model to a real-world case study involving the unscheduled and scheduled removals of a set of engine bleed valves from a fleet of Boeing 737 aircraft. The model can reach an area under the (micro-average) receiver operating characteristic curve of 72%. Our results suggest that the proposed multiclass gated recurrent unit network can provide valuable information about the different fault stages (corresponding to intervals of residual lives) of the studied valves. Full article
(This article belongs to the Special Issue Aerospace Prognosis Technology)
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