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

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

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 31050

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

Special Issue Information

Dear Colleagues,

The present Special Issue entitled “Fault Detection and Prognostics in Aerospace Engineering” focuses on topics related to prognostics, diagnostics, and innovative approaches to fault detection/identification in all sectors of aerospace engineering.

Effective and reliable diagnostic strategies, able to identify the incoming failures and neutralize or, at least, mitigate their effects in a timely manner, are essential in aerospace to guarantee a proper fulfilment of safety requirements. These methods are evolving in parallel with the increase in complexity and criticality of on-board systems and, especially in the last few 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. PHM relies on the continuous monitoring of functional parameters of the system to detect and identify the precursors of failures at an early stage, to estimate the remaining useful life (RUL) of the components. This information about the system health condition can be leveraged in maintenance planning. As a result, most of the necessary maintenance interventions can be scheduled ahead instead of being performed as corrective maintenance. The operation profile of the aircraft can be adaptively modified to reduce ground time, resulting in higher availability and lower operating costs. The adoption of a reliable prognostic strategy supporting the aircraft maintenance activity would lead to a more straightforward troubleshooting task, reducing the total ground time of the vehicle and mitigating the risks associated with the human factor in fault identification.

These topics are now in the spotlight of the scientific community and arouse a growing interest in several industrial sectors (e.g., aerospace, automotive, automation, and more). Therefore, we believe that a collection of selected works providing an overview of the state of the art 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
  • Modeling techniques
  • Monitoring systems
  • Multidomain 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 additional areas. Likewise, we do not want to limit the Special Issue’s focus to diagnostic and prognostic problems only, but we also aim to include significant studies concerning the analysis of the main failure modes affecting aerospace systems, their impact on the systems operation, and the innovative techniques to 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
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.

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Related Special Issue

Published Papers (7 papers)

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Research

18 pages, 13452 KiB  
Article
Online Model-Based Remaining-Useful-Life Prognostics for Aircraft Cooling Units Using Time-Warping Degradation Clustering
by Mihaela Mitici and Ingeborg de Pater
Aerospace 2021, 8(6), 168; https://doi.org/10.3390/aerospace8060168 - 17 Jun 2021
Cited by 10 | Viewed by 3340
Abstract
Remaining-useful-life prognostics for aircraft components are central for efficient and robust aircraft maintenance. In this paper, we propose an end-to-end approach to obtain online, model-based remaining-useful-life prognostics by learning from clusters of components with similar degradation trends. Time-series degradation measurements are first clustered [...] Read more.
Remaining-useful-life prognostics for aircraft components are central for efficient and robust aircraft maintenance. In this paper, we propose an end-to-end approach to obtain online, model-based remaining-useful-life prognostics by learning from clusters of components with similar degradation trends. Time-series degradation measurements are first clustered using dynamic time-warping. For each cluster, a degradation model and a corresponding failure threshold are proposed. These cluster-specific degradation models, together with a particle filtering algorithm, are further used to obtain online remaining-useful-life prognostics. As a case study, we consider the operational data of several cooling units originating from a fleet of aircraft. The cooling units are clustered based on their degradation trends and remaining-useful-life prognostics are obtained in an online manner. In general, this approach provides support for intelligent aircraft maintenance where the analysis of cluster-specific component degradation models is integrated into the predictive maintenance process. Full article
(This article belongs to the Special Issue Fault Detection and Prognostics in Aerospace Engineering)
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14 pages, 1691 KiB  
Article
Methods of Identifying Correlated Model Parameters with Noise in Prognostics
by Ting Dong and Nam H. Kim
Aerospace 2021, 8(5), 129; https://doi.org/10.3390/aerospace8050129 - 5 May 2021
Cited by 1 | Viewed by 3355
Abstract
In physics-based prognostics, model parameters are estimated by minimizing the error or maximizing the likelihood between model predictions and measured data. When multiple model parameters are strongly correlated, it is challenging to identify individual parameters by measuring degradation data, especially when the data [...] Read more.
In physics-based prognostics, model parameters are estimated by minimizing the error or maximizing the likelihood between model predictions and measured data. When multiple model parameters are strongly correlated, it is challenging to identify individual parameters by measuring degradation data, especially when the data have noise. This paper first presents various correlations that occur during the process of model parameter estimation and then introduces two methods of identifying the accurate values of individual parameters when they are strongly correlated. The first method can be applied when the correlation relationship evolves as damage grows, while the second method can be applied when the operating (loading) conditions change. Starting from manufactured data using the true parameters, the accuracy of identified parameters is compared with various levels of noise. It turned out that the proposed method can identify the accurate values of model parameters even with a relatively large level of noise. In terms of the marginal distribution, the standard deviation of a model parameter is reduced from 0.125 to 0.03 when different damage states are used. When the loading conditions change, the uncertainty is reduced from 0.3 to 0.05. Both are considered as a significant improvement. Full article
(This article belongs to the Special Issue Fault Detection and Prognostics in Aerospace Engineering)
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15 pages, 1852 KiB  
Article
A Text-Driven Aircraft Fault Diagnosis Model Based on a Word2vec and Priori-Knowledge Convolutional Neural Network
by Zhenzhong Xu, Bang Chen, Shenghan Zhou, Wenbing Chang, Xinpeng Ji, Chaofan Wei and Wenkui Hou
Aerospace 2021, 8(4), 112; https://doi.org/10.3390/aerospace8040112 - 14 Apr 2021
Cited by 15 | Viewed by 3426
Abstract
In the process of aircraft maintenance and support, a large amount of fault description text data is recorded. However, most of the existing fault diagnosis models are based on structured data, which means they are not suitable for unstructured data such as text. [...] Read more.
In the process of aircraft maintenance and support, a large amount of fault description text data is recorded. However, most of the existing fault diagnosis models are based on structured data, which means they are not suitable for unstructured data such as text. Therefore, a text-driven aircraft fault diagnosis model is proposed in this paper based on Word to Vector (Word2vec) and prior-knowledge Convolutional Neural Network (CNN). The fault text first enters Word2vec to perform text feature extraction, and the extracted text feature vectors are then input into the proposed prior-knowledge CNN to train the fault classifier. The prior-knowledge CNN introduces expert fault knowledge through Cloud Similarity Measurement (CSM) to improve the performance of the fault classifier. Validation experiments on five-year maintenance log data of a civil aircraft were carried out to successfully verify the effectiveness of the proposed model. Full article
(This article belongs to the Special Issue Fault Detection and Prognostics in Aerospace Engineering)
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18 pages, 852 KiB  
Article
Fault Diagnosis and Reconfigurable Control for Commercial Aircraft with Multiple Faults and Actuator Saturation
by Yishi Liu, Sheng Hong, Enrico Zio and Jianwei Liu
Aerospace 2021, 8(4), 108; https://doi.org/10.3390/aerospace8040108 - 14 Apr 2021
Cited by 8 | Viewed by 2910
Abstract
Active fault-tolerant control systems perform fault diagnosis and reconfigurable control. There is a bidirectional uncertainty between them, and an integrated scheme is proposed here to account for that. The system considers both actuator and sensor faults, as well as the external disturbance. The [...] Read more.
Active fault-tolerant control systems perform fault diagnosis and reconfigurable control. There is a bidirectional uncertainty between them, and an integrated scheme is proposed here to account for that. The system considers both actuator and sensor faults, as well as the external disturbance. The diagnostic module is designed using an unknown input observer, and the controller is constructed on the basis of an adaptive method. The integrated strategy is presented, and the stability of the overall system is analyzed. Moreover, different kinds of anti-windup techniques are utilized to modify the original controllers, because of the different controller structures. A simulation of the integrated anti-windup fault-tolerant control method is demonstrated using a numerical model of Boeing 747. The results show that it can guarantee the stability of the post-fault aircraft and increase the control performance for the overall faulty system. Full article
(This article belongs to the Special Issue Fault Detection and Prognostics in Aerospace Engineering)
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33 pages, 18540 KiB  
Article
Deep Neural Network Feature Selection Approaches for Data-Driven Prognostic Model of Aircraft Engines
by Phattara Khumprom, David Grewell and Nita Yodo
Aerospace 2020, 7(9), 132; https://doi.org/10.3390/aerospace7090132 - 4 Sep 2020
Cited by 29 | Viewed by 7315
Abstract
Predicting Remaining Useful Life (RUL) of systems has played an important role in various fields of reliability engineering analysis, including in aircraft engines. RUL prediction is critically an important part of Prognostics and Health Management (PHM), which is the reliability science that is [...] Read more.
Predicting Remaining Useful Life (RUL) of systems has played an important role in various fields of reliability engineering analysis, including in aircraft engines. RUL prediction is critically an important part of Prognostics and Health Management (PHM), which is the reliability science that is aimed at increasing the reliability of the system and, in turn, reducing the maintenance cost. The majority of the PHM models proposed during the past few years have shown a significant increase in the amount of data-driven deployments. While more complex data-driven models are often associated with higher accuracy, there is a corresponding need to reduce model complexity. One possible way to reduce the complexity of the model is to use the features (attributes or variables) selection and dimensionality reduction methods prior to the model training process. In this work, the effectiveness of multiple filter and wrapper feature selection methods (correlation analysis, relief forward/backward selection, and others), along with Principal Component Analysis (PCA) as a dimensionality reduction method, was investigated. A basis algorithm of deep learning, Feedforward Artificial Neural Network (FFNN), was used as a benchmark modeling algorithm. All those approaches can also be applied to the prognostics of an aircraft gas turbine engines. In this paper, the aircraft gas turbine engines data from NASA Ames prognostics data repository was used to test the effectiveness of the filter and wrapper feature selection methods not only for the vanilla FFNN model but also for Deep Neural Network (DNN) model. The findings show that applying feature selection methods helps to improve overall model accuracy and significantly reduced the complexity of the models. Full article
(This article belongs to the Special Issue Fault Detection and Prognostics in Aerospace Engineering)
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15 pages, 495 KiB  
Article
Plant Model-Based Fault Detection during Aircraft Takeoff Using Non-Deterministic Finite-State Automata
by Ferdinand Settele, Alexander Weber and Alexander Knoll
Aerospace 2020, 7(8), 109; https://doi.org/10.3390/aerospace7080109 - 31 Jul 2020
Cited by 2 | Viewed by 3064
Abstract
In this note, the application of a plant model-based fault detection method for nonlinear control systems on aircraft takeoff is introduced. This method utilizes non-deterministic finite-state automata, which approximate the fault-free dynamics of the plant. The aforementioned automaton is computed in a preliminary [...] Read more.
In this note, the application of a plant model-based fault detection method for nonlinear control systems on aircraft takeoff is introduced. This method utilizes non-deterministic finite-state automata, which approximate the fault-free dynamics of the plant. The aforementioned automaton is computed in a preliminary step while during evolution of the plant the automaton is continually evaluated to detect discrepancies between the actual and the nominal dynamics. In this way the fault detection module itself can be implemented on simpler hardware on board of the plant. Moreover, an implementation technique is presented that allows the use of the proposed fault detection method when the plant dynamics is given only by means of a graphical programming script. The great potential and practicality of the used method are demonstrated on a simulated takeoff manoeuvre of a battery-electrically driven aircraft. Full article
(This article belongs to the Special Issue Fault Detection and Prognostics in Aerospace Engineering)
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13 pages, 3199 KiB  
Article
Detection and Prognosis of Propagating Faults in Flight Control Actuators for Helicopters
by Andrea Nesci, Andrea De Martin, Giovanni Jacazio and Massimo Sorli
Aerospace 2020, 7(3), 20; https://doi.org/10.3390/aerospace7030020 - 26 Feb 2020
Cited by 21 | Viewed by 5651
Abstract
Recent trend in the aeronautic industry is to introduce a novel prognostic solution for critical systems in the attempt to increase vehicle availability, reduce costs, and optimize the maintenance policy. Despite this, there is a general lack of literature about prognostics for hydraulic [...] Read more.
Recent trend in the aeronautic industry is to introduce a novel prognostic solution for critical systems in the attempt to increase vehicle availability, reduce costs, and optimize the maintenance policy. Despite this, there is a general lack of literature about prognostics for hydraulic flight control systems, especially looking at helicopter applications. The present research was focused on a preliminary study for an integrated framework of fault detection and failure prognosis tailored for one of the most common architectures for flight control actuation. Starting from a high-fidelity dynamic model of the system, two different faults were studied and described within a dedicated simulation environment: the opening of a crack in the coils of the centering springs of the actuator and the wear of the inner seals. Both failure modes were analyzed through established models available in the literature and their evolution simulated within the model of the actuator. Hence, an in-depth feature selection process was pursued aimed at the definition of signals suitable for both diagnosis and prognosis. Results were then reported through an accuracy-sensitivity plane and used to define a prognostic routine based on particle filtering techniques. The more significant contribution of the present research was that no additional sensors are needed so that the prognostic system can be potentially implemented for in-service platforms. Full article
(This article belongs to the Special Issue Fault Detection and Prognostics in Aerospace Engineering)
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