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Sensing and Data Elaboration in Structural Health Monitoring Technology

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 7008

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Special Issue Editors

Dr. Monica Ciminello

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Guest Editor

Adaptive Structures Division, The Italian Aerospace Research Centre (CIRA), 81043 Capua, Italy
Interests: adaptive structures

Dr. Ignazio Dimino

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Guest Editor

Adaptive Structures Tech Research Unit, CIRA, The Italian Aerospace Research Centre, 81043 Capua, CE, Italy
Interests: morphing technology; smart structures; active control; topology optimization
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Dr. Antonio Concilio

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Guest Editor

Department of Adaptive Structures, Centro Italiano Ricerche Aerospaziali, 81043 Capua, CE, Italy
Interests: adaptive structures; smart structures; morphing; structural health monitoring; integrated vehicle health monitoring; vibroacoustic control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, sensing technology is increasingly being investigated for health monitoring purposes, ranging from aerospace to civil structures applications. A proper selection of sensors (from fibres to pzt or graphene-based, etc.) and an optimal definition of network layout can affect the observability of the target structures significantly, therefore impacting the monitoring process sensitivity, accuracy, and reliability of results.

Sensors can be bonded to or embedded within the reference system, providing data for real-time elaboration from manufacturing, assembly, verification tests, and operation. Suitable algorithms can make data processing smart by appropriate filtering and clustering.

The question remains: what is still missing? The industrial requirements of sensors continue to be unmet, driving designers to focus on top-level aspects like maintainability, reparability, interchangeability and certifications.

This Special Issue aims to put together original research and review articles on SHM applications that address industrial requirements including new technologies, solutions, applications, and new challenges in the field of SHM systems.

Dr. Monica Ciminello
Dr. Ignazio Dimino
Dr. Antonio Concilio
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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

sensor technology
sensors integration
network layout optimization
qualification and certification
real-time data processing
damage detection analysis

Published Papers (5 papers)

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Research

23 pages, 8576 KiB

Open AccessArticle

Different Scenarios of Autonomous Operation of an Environmental Sensor Node Using a Piezoelectric-Vibration-Based Energy Harvester

by Sofiane Bouhedma, Jawad Bin Taufik, Fred Lange, Mohammed Ouali, Hermann Seitz and Dennis Hohlfeld

Sensors 2024, 24(4), 1338; https://doi.org/10.3390/s24041338 - 19 Feb 2024

Viewed by 747

Abstract

This paper delves into the application of vibration-based energy harvesting to power environmental sensor nodes, a critical component of modern data collection systems. These sensor nodes play a crucial role in structural health monitoring, providing essential data on external conditions that can affect the health and performance of structures. We investigate the feasibility and efficiency of utilizing piezoelectric vibration energy harvesters to sustainably power environmental wireless sensor nodes on the one hand. On the other hand, we exploit different approaches to minimize the sensor node’s power consumption and maximize its efficiency. The investigations consider various sensor node platforms and assess their performance under different voltage levels and broadcast frequencies. The findings reveal that optimized harvester designs enable real-time data broadcasting with short intervals, ranging from 1 to 3 s, expanding the horizons of environmental monitoring, and show that in case the system includes a battery as a backup plan, the battery’s lifetime can be extended up to 9 times. This work underscores the potential of vibration energy harvesting as a viable solution for powering sensor nodes, enhancing their autonomy, and reducing maintenance costs in remote and challenging environments. It opens doors to broader applications of sustainable energy sources in environmental monitoring and data collection systems. Full article

(This article belongs to the Special Issue Sensing and Data Elaboration in Structural Health Monitoring Technology)

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18 pages, 4486 KiB

Open AccessArticle

Displacement Estimation via 3D-Printed RFID Sensors for Structural Health Monitoring: Leveraging Machine Learning and Photoluminescence to Overcome Data Gaps

by Metin Pekgor, Reza Arablouei, Mostafa Nikzad and Syed Masood

Sensors 2024, 24(4), 1233; https://doi.org/10.3390/s24041233 - 15 Feb 2024

Viewed by 633

Abstract

Monitoring object displacement is critical for structural health monitoring (SHM). Radio frequency identification (RFID) sensors can be used for this purpose. Using more sensors enhances displacement estimation accuracy, especially when it is realized through the use of machine learning (ML) algorithms for predicting the direction of arrival of the associated signals. Our research shows that ML algorithms, in conjunction with adequate RFID passive sensor data, can precisely evaluate azimuth angles. However, increasing the number of sensors can lead to gaps in the data, which typical numerical methods such as interpolation and imputation may not fully resolve. To overcome this challenge, we propose enhancing the sensitivity of 3D-printed passive RFID sensor arrays using a novel photoluminescence-based RF signal enhancement technique. This can boost received RF signal levels by 2 dB to 8 dB, depending on the propagation mode (near-field or far-field). Hence, it effectively mitigates the issue of missing data without necessitating changes in transmit power levels or the number of sensors. This approach, which enables remote shaping of radiation patterns via light, can herald new prospects in the development of smart antennas for various applications apart from SHM, such as biomedicine and aerospace. Full article

(This article belongs to the Special Issue Sensing and Data Elaboration in Structural Health Monitoring Technology)

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21 pages, 4178 KiB

Open AccessArticle

Data Anomaly Detection for Structural Health Monitoring Based on a Convolutional Neural Network

by Soon-Young Kim and Mukhriddin Mukhiddinov

Sensors 2023, 23(20), 8525; https://doi.org/10.3390/s23208525 - 17 Oct 2023

Cited by 1 | Viewed by 1706

Abstract

Structural health monitoring (SHM) has been extensively utilized in civil infrastructures for several decades. The status of civil constructions is monitored in real time using a wide variety of sensors; however, determining the true state of a structure can be difficult due to the presence of abnormalities in the acquired data. Extreme weather, faulty sensors, and structural damage are common causes of these abnormalities. For civil structure monitoring to be successful, abnormalities must be detected quickly. In addition, one form of abnormality generally predominates the SHM data, which might be a problem for civil infrastructure data. The current state of anomaly detection is severely hampered by this imbalance. Even cutting-edge damage diagnostic methods are useless without proper data-cleansing processes. In order to solve this problem, this study suggests a hyper-parameter-tuned convolutional neural network (CNN) for multiclass unbalanced anomaly detection. A multiclass time series of anomaly data from a real-world cable-stayed bridge is used to test the 1D CNN model, and the dataset is balanced by supplementing the data as necessary. An overall accuracy of 97.6% was achieved by balancing the database using data augmentation to enlarge the dataset, as shown in the research. Full article

(This article belongs to the Special Issue Sensing and Data Elaboration in Structural Health Monitoring Technology)

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12 pages, 6234 KiB

Open AccessArticle

Crack Detecting Method Based on Grid-Type Sensing Networks Using Electrical Signals

by Ju-Hun Ahn, Yong-Chan Lee, Se-Min Jeong, Han-Na Kim and Chang-Yull Lee

Sensors 2023, 23(13), 6093; https://doi.org/10.3390/s23136093 - 02 Jul 2023

Viewed by 1241

Abstract

Cracks have a primary effect on the failure of a structure. Therefore, the development of crack sensors with high accuracy and resolution and cracks detection method are important. In this study, the crack sensors were fabricated, and the crack locations were detected with the electrical signal of the crack sensor. First, a metal grid-type micro-crack sensor based on silver was fabricated. The sensor is made with electrohydrodynamics (EHD) inkjet printing technology, which is well known as the next generation of printed electronics technology. Optimal printing conditions were established through experiments, and a grid sensor was obtained. After that, single cracks and multiple cracks were simulated on the sensor, and electrical signals generated from the sensor were measured. The measured electrical signal tracked the location of the cracks in three steps: simple cross-calculation, interpolation, and modified P-SPICE. It was confirmed that cracks could be effectively found and displayed using the method presented in this paper. Full article

(This article belongs to the Special Issue Sensing and Data Elaboration in Structural Health Monitoring Technology)

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17 pages, 6119 KiB

Open AccessArticle

Electrospun Polyvinylidene Fluoride Piezoelectric Fiber Glass/Carbon Hybrid Self-Sensing Composites for Structural Health Monitoring

by Wei-Han Cheng, Ping-Lun Wu and Hsin-Haou Huang

Sensors 2023, 23(8), 3813; https://doi.org/10.3390/s23083813 - 07 Apr 2023

Cited by 1 | Viewed by 1755

Abstract

In this study, a polyvinylidene fluoride (PVDF)/graphene nanoplatelet (GNP) micro-nanocomposite membrane was fabricated through electrospinning technology and was employed in the fabrication of a fiber-reinforced polymer composite laminate. Some glass fibers were replaced with carbon fibers to serve as electrodes in the sensing layer, and the PVDF/GNP micro-nanocomposite membrane was embedded in the laminate to confer multifunctional piezoelectric self-sensing ability. The self-sensing composite laminate has both favorable mechanical properties and sensing ability. The effects of different concentrations of modified multiwalled carbon nanotubes (CNTs) and GNPs on the morphology of PVDF fibers and the β-phase content of the membrane were investigated. PVDF fibers containing 0.05% GNPs were the most stable and had the highest relative β-phase content; these fibers were embedded in glass fiber fabric to prepare the piezoelectric self-sensing composite laminate. To test the laminate’s practical application, four-point bending and low-velocity impact tests were performed. The results revealed that when damage occurred during bending, the piezoelectric response changed, confirming that the piezoelectric self-sensing composite laminate has preliminary sensing performance. The low-velocity impact experiment revealed the effect of impact energy on sensing performance. Full article

(This article belongs to the Special Issue Sensing and Data Elaboration in Structural Health Monitoring Technology)

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