Special Issue "Selected Papers from IWSHM 2017"

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

Deadline for manuscript submissions: closed (28 February 2018)

Special Issue Editors

Guest Editor
Prof. Dr. Fu-Kuo Chang

Structures and Composites Laboratory, Department of Aeronautics and Astronautics, Stanford University, Stanford, CA 94305, USA
Website | E-Mail
Phone: 650 7968899
Interests: structural health monitoring; design of integrated structures; smart structures; design and damage tolerance of composites structures; multi-functional materials
Guest Editor
Dr. Fotis Kopsaftopoulos

Department of Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
Website | E-Mail
Interests: Structural Health Monitoring (SHM); System Identification & Signal Processing; Smart Materials & Structures

Special Issue Information

Dear Colleagues,

The International Workshop on Structural Health Monitoring (IWSHM) is a biennial workshop aiming to assess the current state-of-the-art technologies in the field of structural health monitoring (SHM), and to discuss and identify key and emerging breakthroughs and challenges in research and development that are critical and unique in SHM. This Special Issue is cooperating with the 11th IWSHM to be held in Stanford, CA, United States, 12–14 September 2017. Authors of outstanding papers related to aerospace SHM presented at the Workshop are invited to submit extended versions of their work to the Special Issue for publication.

Manuscripts are sought that report new research in the field of aerospace on:

  • Selection process of monitored structure
  • Specification and validation of monitoring concepts
  • Selection process of suitable sensing systems
  • Integration of SHM into a Usage/PHM monitoring and management system
  • Handling and management of data/SHM information for maintenance and operational decision support
  • Certification approach

Prof. Fu-Kuo Chang
Dr. Fotis Kopsaftopoulos
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 papers will be 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 quarterly 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 550 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

  • Sensors
  • Maintenance Management System
  • PHM/SHM
  • Certification
  • FAA
  • EASA
  • CBM

Published Papers (5 papers)

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Research

Open AccessArticle Single-Sensor Acoustic Emission Source Localization in Plate-Like Structures Using Deep Learning
Received: 27 March 2018 / Revised: 19 April 2018 / Accepted: 19 April 2018 / Published: 1 May 2018
Cited by 4 | PDF Full-text (6370 KB) | HTML Full-text | XML Full-text
Abstract
This paper introduces two deep learning approaches to localize acoustic emissions (AE) sources within metallic plates with geometric features, such as rivet-connected stiffeners. In particular, a stack of autoencoders and a convolutional neural network are used. The idea is to leverage the reflection
[...] Read more.
This paper introduces two deep learning approaches to localize acoustic emissions (AE) sources within metallic plates with geometric features, such as rivet-connected stiffeners. In particular, a stack of autoencoders and a convolutional neural network are used. The idea is to leverage the reflection and reverberation patterns of AE waveforms as well as their dispersive and multimodal characteristics to localize their sources with only one sensor. Specifically, this paper divides the structure into multiple zones and finds the zone in which each source occurs. To train, validate, and test the deep learning networks, fatigue cracks were experimentally simulated by Hsu–Nielsen pencil lead break tests. The pencil lead breaks were carried out on the surface and at the edges of the plate. The results show that both deep learning networks can learn to map AE signals to their sources. These results demonstrate that the reverberation patterns of AE sources contain pertinent information to the location of their sources. Full article
(This article belongs to the Special Issue Selected Papers from IWSHM 2017)
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Figure 1

Open AccessArticle Uncertainty Evaluation in the Design of Structural Health Monitoring Systems for Damage Detection
Received: 28 February 2018 / Revised: 13 April 2018 / Accepted: 17 April 2018 / Published: 21 April 2018
Cited by 1 | PDF Full-text (2374 KB) | HTML Full-text | XML Full-text
Abstract
The validation of structural health monitoring (SHM) systems for aircraft is complicated by the extent and number of factors that the SHM system must demonstrate for robust performance. Therefore, a time- and cost-efficient method for examining all of the sensitive factors must be
[...] Read more.
The validation of structural health monitoring (SHM) systems for aircraft is complicated by the extent and number of factors that the SHM system must demonstrate for robust performance. Therefore, a time- and cost-efficient method for examining all of the sensitive factors must be conducted. In this paper, we demonstrate the utility of using the simulation modeling environment to determine the SHM sensitive factors that must be considered for subsequent experiments, in order to enable the SHM validation. We demonstrate this concept by examining the effect of SHM system configuration and flaw characteristics on the response of a signal from a known piezoelectric wafer active sensor (PWAS) in an aluminum plate, using simulation models of a particular hot spot. We derive the signal responses mathematically and through the statistical design of experiments, we determine the significant factors that affect the damage indices that are computed from the signal, using only half the number of runs that are normally required. We determine that the transmitter angle is the largest source of variation for the damage indices that are considered, followed by signal frequency and transmitter distance to the hot spot. These results demonstrate that the use of efficient statistical design and simulation may enable a cost- and time-efficient sequential approach to quantifying sensitive SHM factors and system validation. Full article
(This article belongs to the Special Issue Selected Papers from IWSHM 2017)
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Open AccessArticle Damage Detection in a Composite T-Joint Using Guided Lamb Waves
Received: 22 February 2018 / Revised: 3 April 2018 / Accepted: 5 April 2018 / Published: 9 April 2018
Cited by 1 | PDF Full-text (30717 KB) | HTML Full-text | XML Full-text
Abstract
Low velocity impact induces barely visible damage in the form of matrix cracking or delamination that can grow under hydro-thermo-mechanical loading and possibly lead to catastrophic failure if not detected at an early stage. A network of piezoelectric transducers can be used to
[...] Read more.
Low velocity impact induces barely visible damage in the form of matrix cracking or delamination that can grow under hydro-thermo-mechanical loading and possibly lead to catastrophic failure if not detected at an early stage. A network of piezoelectric transducers can be used to monitor a structure over time for life prognosis through generation and sensing of guided ultrasonic waves. The aim of this study is to design and develop such a sensing method for damage assessment in a composite T-joint subjected to mechanical impacts. In this context, monitoring of Lamb waves in a carbon fibre reinforced polymer (CFRP) T-joint has been completed where dispersion and tuning curves have been obtained. Guided waves are transmitted into the structure through different specified pairs of surface-bonded lead-zirconate-titanate (PZT) transducers in a pitch–catch active structural health monitoring (SHM) approach. With these experiments, Lamb wave fundamental modes (A0 and S0) are identified for monitoring impact damage by signal comparison with a prior obtained baseline. Detecting 4J and 10J inner impacts within the central region of the specimen is challenging when using conventional non-destructive techniques (NDT) because of the complex geometry and interference with the web. Signals are compared for the same selected sensing path; and amplitude differences have been observed in tuning curves after the 10J impact, which implies the occurrence of a structural change related to the impact. Full article
(This article belongs to the Special Issue Selected Papers from IWSHM 2017)
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Graphical abstract

Open AccessArticle Transducer Placement Option of Lamb Wave SHM System for Hotspot Damage Monitoring
Received: 27 February 2018 / Revised: 24 March 2018 / Accepted: 28 March 2018 / Published: 4 April 2018
Cited by 1 | PDF Full-text (79176 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we investigated transducer placement strategies for detecting cracks in primary aircraft structures using ultrasonic Structural Health Monitoring (SHM). The approach developed is for an expected damage location based on fracture mechanics, for example fatigue crack growth in a high stress
[...] Read more.
In this paper, we investigated transducer placement strategies for detecting cracks in primary aircraft structures using ultrasonic Structural Health Monitoring (SHM). The approach developed is for an expected damage location based on fracture mechanics, for example fatigue crack growth in a high stress location. To assess the performance of the developed approach, finite-element (FE) modelling of a damage-tolerant aluminum fuselage has been performed by introducing an artificial crack at a rivet hole into the structural FE model and assessing its influence on the Lamb wave propagation, compared to a baseline measurement simulation. The efficient practical sensor position was determined from the largest change in area that is covered by reflected and missing wave scatter using an additive color model. Blob detection algorithms were employed to determine the boundaries of this area and to calculate the blob centroid. To demonstrate that the technique can be generalized, the results from different crack lengths and from tilted crack are also presented. Full article
(This article belongs to the Special Issue Selected Papers from IWSHM 2017)
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Graphical abstract

Open AccessArticle Mechanical and Non-Destructive Study of CFRP Adhesive Bonds Subjected to Pre-Bond Thermal Treatment and De-Icing Fluid Contamination
Received: 27 February 2018 / Revised: 23 March 2018 / Accepted: 28 March 2018 / Published: 2 April 2018
Cited by 1 | PDF Full-text (16485 KB) | HTML Full-text | XML Full-text
Abstract
Composite materials are commonly used in many branches of industry. One of the effective methods to join the carbon fibre reinforced polymer (CFRP) parts includes the use of adhesives. There is a search on effective methods for quality assurance of bonded parts. In
[...] Read more.
Composite materials are commonly used in many branches of industry. One of the effective methods to join the carbon fibre reinforced polymer (CFRP) parts includes the use of adhesives. There is a search on effective methods for quality assurance of bonded parts. In the research here reported the influence of surface pre-bond modification on the adhesive bonds of CFRP plates has been analyzed. Adherends surface modifications, to include defects affecting the bonding quality, were obtained through surface thermal treatment, surface contamination with de-icing fluid and a combination of both the previously described treatments. Characterization of bonded joints was performed by means of mechanical testing, ultrasounds and electromechanical impedance (EMI) measurements. The study here proposed has also the aim to evaluate the ability of different destructive and non-destructive techniques to assess the quality of the bonds. While mechanical tests were strongly affected by the surface modifications, results obtained ultrasound and EMI test have demonstrate only a limited ability of these techniques to differentiate between the different samples. In fact, ultrasounds did not show any changes in the bondline, due to pre-bond modifications. However, this technique was able to detect delamination in CFRP for one of the samples thermally treated at 280 °C. Electromechanical impedance (EMI) measurements showed similar behavior as mechanical tests for samples thermally treated at 260 °C and 280 °C, and for the sample whose surface modification was made with a combination of thermally and de-icing fluid treatments. Full article
(This article belongs to the Special Issue Selected Papers from IWSHM 2017)
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Graphical abstract

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