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Aerospace
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Selected Papers from IWSHM 2019
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Selected Papers from IWSHM 2019

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

Deadline for manuscript submissions: closed (31 March 2020) | Viewed by 21495

Special Issue Editors

Prof. Dr. Fu-Kuo Chang

E-Mail Website
Guest Editor
Structures and Composites Laboratory, Department of Aeronautics and Astronautics, Stanford University, Stanford, CA 94305, USA
Interests: structural health monitoring; design of integrated structures; smart structures; design and damage tolerance of composites structures; multi-functional materials
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wing Chiu

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, Monash University, Wellington Rd, Clayton, VIC 3800, Australia
Interests: structural health monitoring; non-destructive testing; evaluation stress wave research; composite structures
Special Issues, Collections and Topics in MDPI journals
Dr. Mohammad Faisal Haider

E-Mail Website
Guest Editor
Structures and Composites Laboratory, Department of Aeronautics and Astronautics, Stanford University, Stanford, CA 94305, USA
Interests: multi-functional composites; multi-functional intelligent structures; structural health monitoring; battery health monitoring; machine learning
Special Issues, Collections and Topics in MDPI journals

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.

The 12th International Workshop on Structural Health Monitoring will be held at the Stanford University, CA, USA, from September 10-12, 2019. The workshop features reviews of SHM growth in the last two decades and the perspectives of future SHM directions in research and applications. Papers in support of autonomous and/or intelligent systems and the Industry Internet of Things (IIOT) will be highlighted besides general applications in aerospace, mechanical and civil infrastructure.

This Special Issue is cooperating with the 12th IWSHM. 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, but not limited to:

Sensors/Actuators

  • Novel smart sensors
  • Sensors for extreme environments
  • MEMS/NEMS sensors
  • Fiber optics
  • Piezoelectric/magneto-electric sensors
  • CNT sensors

 Aerial Sensing

  • Robotic and UAV platforms for SHM and maintenance intervention

 Sensor Networks/System Integration

  • Bio-inspired sensor networks
  • Remote & wireless communication
  • Self-diagnostic networks
  • Self-configurable & fault-tolerance networks
  • Advanced manufacturing techniques
  • Sensor network reliability

 Multifunctional Materials and Structures

  • Multifunctional materials
  • Self-sensing materials
  • Novel energy storage systems
  • Energy harvesting and self-diagnostic structures
  • Novel composite materials

Diagnostics/Signal Processing/State Awareness

  • Advanced signal processing
  • Statistical signal processing
  • Data mining/fusion
  • Real-time diagnostics
  • System identification
  • Inverse methods
  • Big data analysis

Prognostics/Health Management/Safety Assurance

  • Remaining life estimation
  • Quality control
  • Life-cycle monitoring
  • Integrated structural health management
  • System-wide safety assurance
  • Condition assessment

 Cyber-physical systems for aerospace SHM

  • Data-driven simulations and diagnostics
  • Integration of data-driven and physics-based methods
  • Multi-physics, multi-scale modeling approaches
  • Manufacturing with sensor data
  • Multi-objective design optimization
  • SHM-based design.

Implementation/Validation/Certification

  • Quantification techniques
  • Probability of detection (POD)
  • Reliability methods
  • Validation/certification processes, etc.

Applications

  • Application to Transportation Systems: Aircraft and space vehicles, rotorcraft, satellites, space
  • Autonomous Systems: Drones, UAVs, self-driving cars, robotics, energy management systems

Prof. Dr. Fu-Kuo Chang
Prof. Dr. Wing Chiu
Dr. Mohammad Faisal Haider
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.

Related Special Issue

Published Papers (4 papers)

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Research

27 pages, 16191 KiB  
Article
A Nonlinear Ultrasonic Modulation Method for Crack Detection in Turbine Blades
by Frank Mevissen and Michele Meo
Aerospace 2020, 7(6), 72; https://doi.org/10.3390/aerospace7060072 - 4 Jun 2020
Cited by 18 | Viewed by 4579
Abstract
In modern gas turbines, efforts are being made to improve efficiency even further. This is achieved primarily by increasing the generated pressure ratio in the compressor and by increasing the turbine inlet temperature. This leads to enormous loads on the components in the [...] Read more.
In modern gas turbines, efforts are being made to improve efficiency even further. This is achieved primarily by increasing the generated pressure ratio in the compressor and by increasing the turbine inlet temperature. This leads to enormous loads on the components in the hot gas region in the turbine. As a result, non-destructive testing and structural health monitoring (SHM) processes are becoming increasingly important to gas turbine manufacturers. Initial cracks in the turbine blades must be identified before catastrophic events occur. A proven method is the linear ultrasound method. By monitoring the amplitude and phase fluctuations of the input signal, structural integrity of the components can be detected. However, closed cracks or small cracks cannot be easily detected due to a low impedance mismatch with the surrounding materials. By contrast, nonlinear ultrasound methods have shown that damages can be identified at an early stage by monitoring new signal components such as sub- and higher harmonics of the fundamental frequency in the frequency spectrum. These are generated by distortion of the elastic waveform due to damage/nonlinearity of the material. In this paper, new global nonlinear parameters were derived that result from the dual excitation of two different ultrasound frequencies. These nonlinear features were used to assess the presence of cracks as well as their qualitative sizes. The proposed approach was tested on several samples and turbine blades with artificial and real defects. The results were compared to samples without failure. Numerical simulations were conducted to investigate nonlinear elastic interaction of the stress waves with the damage regions. The results show a clear trend of nonlinear parameters changing as a function of the crack size, demonstrating the capability of the proposed approach to detect in-service cracks. Full article
(This article belongs to the Special Issue Selected Papers from IWSHM 2019)
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13 pages, 3807 KiB  
Article
The Industry Internet of Things (IIoT) as a Methodology for Autonomous Diagnostics in Aerospace Structural Health Monitoring
by Sarah Malik, Rakeen Rouf, Krzysztof Mazur and Antonios Kontsos
Aerospace 2020, 7(5), 64; https://doi.org/10.3390/aerospace7050064 - 24 May 2020
Cited by 18 | Viewed by 6528
Abstract
Structural Health Monitoring (SHM), defined as the process that involves sensing, computing, and decision making to assess the integrity of infrastructure, has been plagued by data management challenges. The Industrial Internet of Things (IIoT), a subset of Internet of Things (IoT), provides a [...] Read more.
Structural Health Monitoring (SHM), defined as the process that involves sensing, computing, and decision making to assess the integrity of infrastructure, has been plagued by data management challenges. The Industrial Internet of Things (IIoT), a subset of Internet of Things (IoT), provides a way to decisively address SHM’s big data problem and provide a framework for autonomous processing. The key focus of IIoT is operational efficiency and cost optimization. The purpose, therefore, of the IIoT approach in this investigation is to develop a framework that connects nondestructive evaluation sensor data with real-time processing algorithms on an IoT hardware/software system to provide diagnostic capabilities for efficient data processing related to SHM. Specifically, the proposed IIoT approach is comprised of three components: the Cloud, the Fog, and the Edge. The Cloud is used to store historical data as well as to perform demanding computations such as off-line machine learning. The Fog is the hardware that performs real-time diagnostics using information received both from sensing and the Cloud. The Edge is the bottom level hardware that records data at the sensor level. In this investigation, an application of this approach to evaluate the state of health of an aerospace grade composite material at laboratory conditions is presented. The key link that limits human intervention in data processing is the implemented database management approach which is the particular focus of this manuscript. Specifically, a NoSQL database is implemented to provide live data transfer from the Edge to both the Fog and Cloud. Through this database, the algorithms used are capable to execute filtering by classification at the Fog level, as live data is recorded. The processed data is automatically sent to the Cloud for further operations such as visualization. The system integration with three layers provides an opportunity to create a paradigm for intelligent real-time data quality management. Full article
(This article belongs to the Special Issue Selected Papers from IWSHM 2019)
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12 pages, 1953 KiB  
Article
On the Application of Actively and Passively Excited Guided Elastic Waves for the Monitoring of Fiber-Reinforced Plastics
by Kilian Tschöke, Tobias Gaul, Alexander Pietzsch, Eberhard Schulze and Lars Schubert
Aerospace 2020, 7(5), 53; https://doi.org/10.3390/aerospace7050053 - 30 Apr 2020
Cited by 1 | Viewed by 3639
Abstract
Nowadays, fiber-reinforced plastics are found in numerous industrial applications, such as aerospace, automotive engineering, railway, and naval engineering. These materials have high tensile and flexural strengths and nevertheless a low density at the same time. The use of fiber-reinforced plastics is particularly relevant [...] Read more.
Nowadays, fiber-reinforced plastics are found in numerous industrial applications, such as aerospace, automotive engineering, railway, and naval engineering. These materials have high tensile and flexural strengths and nevertheless a low density at the same time. The use of fiber-reinforced plastics is particularly relevant in areas where large masses have to be moved and accelerated. However, testing and monitoring these structures is still a challenge caused by the different damage behavior compared to metal structures. Non-visible structural changes, such as delaminations and fiber-fractures, may cause local degradation and finally the failure of the components. In this work, active and passive ultrasonic methods based on guided elastic waves are investigated for their applicability to carbon fiber-reinforced structures. Therefore, tensile tests with cyclically increasing load are carried out on specimens with different fiber orientations until complete failure. The acoustic emissions in the specimen during the load are recorded. As a second technique, actively excited guided waves are transmitted and received during the rest periods between the measuring ramps. Different parameters are extracted from the measured data, which allow the monitoring of the specimen’s degradation. A comparison of the results of the active and passive method follows. Finally, a combination of both methods is carried out addressing issues like its informative value and its sensitivity. Full article
(This article belongs to the Special Issue Selected Papers from IWSHM 2019)
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15 pages, 15395 KiB  
Article
S-Parameter-Based Defect Localization for Ultrasonic Guided Wave SHM
by Gift Nyikayaramba and Boris Murmann
Aerospace 2020, 7(3), 33; https://doi.org/10.3390/aerospace7030033 - 20 Mar 2020
Cited by 3 | Viewed by 5583
Abstract
In this work, an approach for enabling miniaturized, low-voltage hardware for active structural health monitoring (SHM) based on ultrasonic guided waves is investigated. The proposed technique relies on S-parameter measurements instead of time-domain pulsing and thereby trades off longer measurement times with lower [...] Read more.
In this work, an approach for enabling miniaturized, low-voltage hardware for active structural health monitoring (SHM) based on ultrasonic guided waves is investigated. The proposed technique relies on S-parameter measurements instead of time-domain pulsing and thereby trades off longer measurement times with lower actuation voltages for improved compatibility with dense complementary metal-oxide-semiconductor (CMOS) chip integration. To demonstrate the feasibility of this method, we present results showing the successful localization of defects in aluminum and carbon-fiber-reinforced polymer (CFRP) test structures using S-parameter measurements. The S-parameter measurements were made on benchtop vector network analyzers that actuate the piezoelectric transducers at output voltage amplitudes as low as 1.264 Vpp. Full article
(This article belongs to the Special Issue Selected Papers from IWSHM 2019)
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