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Special Issue "Smart Sensors for Damage Detection"

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

Deadline for manuscript submissions: closed (5 November 2021) | Viewed by 17899

Special Issue Editor

Dr. Jochen Moll
E-Mail Website
Guest Editor
Department of Physics, Goethe University of Frankfurt, Max-von-Laue-Str. 1, 60438 Frankfurt am Main, Germany
Interests: structural health monitoring; damage detection; sensor systems; signal processing techniques

Special Issue Information

Dear Colleagues,

This Special Issue is aimed at the submission of both review and original research articles related to smart sensors in the framework of a structural health monitoring (SHM) system. This includes sensor technologies that automatically detect damage based on its direct sensing principle. On the other hand, a smart sensor or smart sensor array may consist of one or multiple identical sensors providing indirect information about damage by suitable signal processing techniques. The Special Issue “Smart Sensors for Damage Detection” welcomes contributions in this field including, for example, acoustic transducers, electromagnetic sensors, and optical sensors.

It is expected that the sensor and its underlying sensing principle has been properly described and its damage detection performance has been tested in a relevant environment. Numerical investigations may also help to provide additional insights. In any case, it is required to show experimental results from a dedicated laboratory experiment possibly combined with a demonstration in the field.

Potential applications include aviation and maritime industry, but also pipeworks, bridges, and other technical structures.

Dr. Jochen Moll
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. 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 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.

Keywords

  • Smart sensors and smart sensor systems
  • Multifunctional sensors
  • Damage detection
  • Composites
  • Self-powered and low-power sensors
  • Embedded sensors, sensor/structure integration
  • Signal processing techniques.

Published Papers (10 papers)

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Research

Jump to: Review, Other

Article
Optimal Array Design and Directive Sensors for Guided Waves DoA Estimation
Sensors 2022, 22(3), 780; https://doi.org/10.3390/s22030780 - 20 Jan 2022
Cited by 3 | Viewed by 837
Abstract
The estimation of Direction of Arrival (DoA) of guided ultrasonic waves is an important task in many Structural Health Monitoring (SHM) applications. The aim is to locate sources of elastic waves which can be generated by impacts or defects in the inspected structures. [...] Read more.
The estimation of Direction of Arrival (DoA) of guided ultrasonic waves is an important task in many Structural Health Monitoring (SHM) applications. The aim is to locate sources of elastic waves which can be generated by impacts or defects in the inspected structures. In this paper, the array geometry and the shape of the piezo-sensors are designed to optimize the DoA estimation on a pre-defined angular sector, from acquisitions affected by noise and interference. In the proposed approach, the DoA of a wave generated by a single source is considered as a random variable that is uniformly distributed in a given range. The wave velocity is assumed to be unknown and the DoA estimation is performed by measuring the Differences in Time of Arrival (DToAs) of wavefronts impinging on the sensors. The optimization procedure of sensors positioning is based on the computation of the DoA and wave velocity parameters Cramér-Rao Matrix Bound (CRMB) with a Bayesian approach. An efficient DoA estimator is found based on the DToAs Gauss-Markov estimator for a three sensors array. Moreover, a novel directive sensor for guided waves is introduced to cancel out undesired Acoustic Sources impinging from DoAs out of the given angles range. Numerical results show the capability to filter directional interference of the novel sensor and a considerably improved DoA estimation performance provided by the optimized sensor cluster in the pre-defined angular sector, as compared to conventional approaches. Full article
(This article belongs to the Special Issue Smart Sensors for Damage Detection)
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Article
Towards Interpretable Machine Learning for Automated Damage Detection Based on Ultrasonic Guided Waves
Sensors 2022, 22(1), 406; https://doi.org/10.3390/s22010406 - 05 Jan 2022
Cited by 4 | Viewed by 2207
Abstract
Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based [...] Read more.
Data-driven analysis for damage assessment has a large potential in structural health monitoring (SHM) systems, where sensors are permanently attached to the structure, enabling continuous and frequent measurements. In this contribution, we propose a machine learning (ML) approach for automated damage detection, based on an ML toolbox for industrial condition monitoring. The toolbox combines multiple complementary algorithms for feature extraction and selection and automatically chooses the best combination of methods for the dataset at hand. Here, this toolbox is applied to a guided wave-based SHM dataset for varying temperatures and damage locations, which is freely available on the Open Guided Waves platform. A classification rate of 96.2% is achieved, demonstrating reliable and automated damage detection. Moreover, the ability of the ML model to identify a damaged structure at untrained damage locations and temperatures is demonstrated. Full article
(This article belongs to the Special Issue Smart Sensors for Damage Detection)
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Article
Experimental Detection and Measurement of Crack-Type Damage Features in Composite Thin-Wall Beams Using Modal Analysis
Sensors 2021, 21(23), 8102; https://doi.org/10.3390/s21238102 - 03 Dec 2021
Cited by 4 | Viewed by 995
Abstract
An experimental proof-of-concept for damage detection in composite beams using modal analysis has been conducted. The purpose was to demonstrate that damage features can be detected, located, and measured on the surface of a relatively complex thin-wall beam made from composite material. (1) [...] Read more.
An experimental proof-of-concept for damage detection in composite beams using modal analysis has been conducted. The purpose was to demonstrate that damage features can be detected, located, and measured on the surface of a relatively complex thin-wall beam made from composite material. (1) Background: previous work has been limited to the study of simple geometries and materials. (2) Methods: damage detection in the work is based on the accurate measurement of mode shapes and an appropriate design of the detection mesh. Both a method requiring information about the healthy structure and a baseline-free method have been implemented. (3) Results: short crack-type damage features, both longitudinal and transverse, were detected reliably, and the true length of the crack can be estimated from the damage signal. Simultaneous detection of two cracks on the same sample is also possible. (4) This work demonstrates the feasibility of automated damage detection in composite beams using sensor arrays. Full article
(This article belongs to the Special Issue Smart Sensors for Damage Detection)
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Article
Multiple Damage Detection of an Offshore Helideck through the Two-Step Artificial Neural Network Based on the Limited Mode Shape Data
Sensors 2021, 21(21), 7357; https://doi.org/10.3390/s21217357 - 05 Nov 2021
Cited by 2 | Viewed by 870
Abstract
A helideck is an essential structure in an offshore platform, and it is crucial to maintain its structural integrity and detect the occurrence of damage early. Because helidecks usually consist of complex lattice truss members, precise measurements are required for structural health monitoring [...] Read more.
A helideck is an essential structure in an offshore platform, and it is crucial to maintain its structural integrity and detect the occurrence of damage early. Because helidecks usually consist of complex lattice truss members, precise measurements are required for structural health monitoring based on accurate modal parameters. However, available sensors and data acquisition are limited. Therefore, we propose a two-step damage detection process using an artificial neural network. Based on the mode shape database collected from 137,400 damage scenarios by finite element analysis, the neural network in the first step was trained to estimate the mode shapes of the entire helideck model using the selected mode shape data obtained from the limited measuring points. Then, the neural network in the second step is consecutively trained to detect the location and amount of structural damage to individual parts. As a result, it is shown that the proposed procedure provides the damage detection capability with only a quarter of the entire mode shape data, while the estimation accuracy is sufficiently high compared to the single network directly trained using all mode shape data. It was also found that, compared to the network directly trained from the same data, the proposed technique tends to detect minor damages more accurately. Full article
(This article belongs to the Special Issue Smart Sensors for Damage Detection)
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Article
Smart Inlays for Simultaneous Crack Sensing and Arrest in Multifunctional Bondlines of Composites
Sensors 2021, 21(11), 3852; https://doi.org/10.3390/s21113852 - 02 Jun 2021
Cited by 4 | Viewed by 1978
Abstract
Disbond arrest features combined with a structural health monitoring system for permanent bondline surveillance have the potential to significantly increase the safety of adhesive bonds in composite structures. A core requirement is that the integration of such features is achieved without causing weakening [...] Read more.
Disbond arrest features combined with a structural health monitoring system for permanent bondline surveillance have the potential to significantly increase the safety of adhesive bonds in composite structures. A core requirement is that the integration of such features is achieved without causing weakening of the bondline. We present the design of a smart inlay equipped with a micro strain sensor-system fabricated on a polyvinyliden fluorid (PVDF) foil material. This material has proven disbond arrest functionality, but has not before been used as a substrate in lithographic micro sensor fabrication. Only with special pretreatment can it meet the requirements of thin film sensor elements regarding surface roughness and adhesion. Moreover, the sensor integration into composite material using a standard manufacturing procedure reveals that the smart inlays endure this process even though subjected to high temperatures, curing reactions and plasma treatment. Most critical is the substrate melting during curing when sensory function is preserved with a covering caul plate that stabilizes the fragile measuring grids. The smart inlays are tested by static mechanical loading, showing that they can be stretched far beyond critical elongations of composites before failure. The health monitoring function is verified by testing the specimens with integrated sensors in a cantilever bending setup. The results prove the feasibility of micro sensors detecting strain gradients on a disbond arresting substrate to form a so-called multifunctional bondline. Full article
(This article belongs to the Special Issue Smart Sensors for Damage Detection)
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Article
Lamb Wave Scattering Analysis for Interface Damage Detection between a Surface-Mounted Block and Elastic Plate
Sensors 2021, 21(3), 860; https://doi.org/10.3390/s21030860 - 28 Jan 2021
Cited by 9 | Viewed by 1276
Abstract
Since stringers are often applied in engineering constructions to improve thin-walled structures’ strength, methods for damage detection at the joints between the stringer and the thin-walled structure are necessary. A 2D mathematical model was employed to simulate Lamb wave excitation and sensing via [...] Read more.
Since stringers are often applied in engineering constructions to improve thin-walled structures’ strength, methods for damage detection at the joints between the stringer and the thin-walled structure are necessary. A 2D mathematical model was employed to simulate Lamb wave excitation and sensing via rectangular piezoelectric-wafer active transducers mounted on the surface of an elastic plate with rectangular surface-bonded obstacles (stiffeners) with interface defects. The results of a 2D simulation using the finite element method and the semi-analytical hybrid approach were validated experimentally using laser Doppler vibrometry for fully bonded and semi-debonded rectangular obstacles. A numerical analysis of fundamental Lamb wave scattering via rectangular stiffeners in different bonding states is presented. Two kinds of interfacial defects between the stiffener and the plate are considered: the partial degradation of the adhesive at the interface and an open crack. Damage indices calculated using the data obtained from a sensor are analyzed numerically. The choice of an input impulse function applied at the piezoelectric actuator is discussed from the perspective of the development of guided-wave-based structural health monitoring techniques for damage detection. Full article
(This article belongs to the Special Issue Smart Sensors for Damage Detection)
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Article
Detecting of the Crack and Leakage in the Joint of Precast Concrete Segmental Bridge Using Piezoceramic Based Smart Aggregate
Sensors 2020, 20(18), 5398; https://doi.org/10.3390/s20185398 - 21 Sep 2020
Cited by 3 | Viewed by 1779
Abstract
Precast concrete segmental bridges (PCSBs) have been widely used in bridge engineering due to their numerous competitive advantages. The structural behavior and health status of PCSBs largely depend on the performance of the joint between the assembled segments. However, due to construction errors [...] Read more.
Precast concrete segmental bridges (PCSBs) have been widely used in bridge engineering due to their numerous competitive advantages. The structural behavior and health status of PCSBs largely depend on the performance of the joint between the assembled segments. However, due to construction errors and dynamic loading conditions, some cracks and leakages have been found at the epoxy joints of PCSBs during the construction or operation stage. These defects will affect the joint quality, negatively impacting the safety and durability of the bridge. A structural health monitoring (SHM) method using active sensing with a piezoceramic-based smart aggregate (SA) to detect the crack and leakage in the epoxy joint of PCSBs was proposed and the feasibility was studied by experiment in the present work. Two concrete prisms were prefabricated with installed SAs and assembled with epoxy joint. An initial defect was simulated by leaving a 3-cm crack at the center of the joint without epoxy. With a total of 13 test cases and the different lengths of cracks without water and filled with water were simulated and tested. Time-domain analysis, frequency-domain analysis and wavelet-packet-based energy index (WPEI) analysis were conducted to evaluate the health condition of the structure. By comparing the collected voltage signals, Power Spectrum Density (PSD) energy and WPEIs under different healthy states, it is shown that the test results are closely related to the length of the crack and the leakage in the epoxy joint. It is demonstrated that the devised approach has certain application value in detecting the crack and leakage in the joint of PCSBs. Full article
(This article belongs to the Special Issue Smart Sensors for Damage Detection)
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Article
A Variable Data Fusion Approach for Electromechanical Impedance-Based Damage Detection
Sensors 2020, 20(15), 4204; https://doi.org/10.3390/s20154204 - 28 Jul 2020
Cited by 12 | Viewed by 1937
Abstract
There is continuing research in the area of structural health monitoring (SHM) as it may allow a reduction in maintenance costs as well as lifetime extension. The search for a low-cost health monitoring system that is able to detect small levels of damage [...] Read more.
There is continuing research in the area of structural health monitoring (SHM) as it may allow a reduction in maintenance costs as well as lifetime extension. The search for a low-cost health monitoring system that is able to detect small levels of damage is still on-going. The present study is one more step in this direction. This paper describes a data fusion technique by combining the information for robust damage detection using the electromechanical impedance (EMI) method. The EMI method is commonly used for damage detection due to its sensitivity to low levels of damage. In this paper, the information of resistance (R) and conductance (G) is studied in a selected frequency band and a novel data fusion approach is proposed. A novel fused parameter (F) is developed by combining the information from G and R. The difference in the new metric under different damage conditions is then quantified using established indices such as the root mean square deviation (RMSD) index, mean absolute percentage deviation (MAPD), and root mean square deviation using k-th state as the reference (RMSDk). The paper presents an application of the new metric for detection of damage in three structures, namely, a thin aluminum (Al) plate with increasing damage severity (simulated with a drilled hole of increasing size), a glass fiber reinforced polymer (GFRP) composite beam with increasing delamination and another GFRP plate with impact-induced damage scenarios. Based on the experimental results, it is apparent that the variable F increases the robustness of the damage detection as compared to the quantities R and G. Full article
(This article belongs to the Special Issue Smart Sensors for Damage Detection)
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Review

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Review
A Review on Low-Cost Microwave Doppler Radar Systems for Structural Health Monitoring
Sensors 2021, 21(8), 2612; https://doi.org/10.3390/s21082612 - 08 Apr 2021
Cited by 6 | Viewed by 2623
Abstract
Portable, low-cost, microwave radars have attracted researchers’ attention for being an alternative noncontact solution for structural condition monitoring. In addition, by leveraging their capability of providing the target velocity information, the radar-based remote monitoring of complex rotating structures can also be accomplished. Modern [...] Read more.
Portable, low-cost, microwave radars have attracted researchers’ attention for being an alternative noncontact solution for structural condition monitoring. In addition, by leveraging their capability of providing the target velocity information, the radar-based remote monitoring of complex rotating structures can also be accomplished. Modern radar systems are compact, able to be easily integrated in sensor networks, and can deliver high accuracy measurements. This paper reviews the recent technical advances in low-cost Doppler radar systems for phase-demodulated displacement measurements and time-Doppler analysis for structural health information, including digital signal processing and emerging applications related to radar sensor networks. Full article
(This article belongs to the Special Issue Smart Sensors for Damage Detection)
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Other

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Letter
Detection of Pin Failure in Carbon Fiber Composites Using the Electro-Mechanical Impedance Method
Sensors 2020, 20(13), 3732; https://doi.org/10.3390/s20133732 - 03 Jul 2020
Cited by 1 | Viewed by 1608
Abstract
This paper presents a proof of concept for simultaneous load and structural health monitoring of a hybrid carbon fiber rudder stock sample consisting of carbon fiber composite and metallic parts in order to demonstrate smart sensors in the context of maritime systems. Therefore, [...] Read more.
This paper presents a proof of concept for simultaneous load and structural health monitoring of a hybrid carbon fiber rudder stock sample consisting of carbon fiber composite and metallic parts in order to demonstrate smart sensors in the context of maritime systems. Therefore, a strain gauge is used to assess bending loads during quasi-static laboratory testing. In addition, six piezoelectric transducers are placed around the circumference of the tubular structure for damage detection based on the electro-mechanical impedance (EMI) method. A damage indicator has been defined that exploits the real and imaginary parts of the admittance for the detection of pin failure in the rudder stock. In particular, higher frequencies in the EMI spectrum contain valuable information about damage. Finally, the information about damage and load are merged in a cluster analysis enabling damage detection under load. Full article
(This article belongs to the Special Issue Smart Sensors for Damage Detection)
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