Research

23 pages, 10523 KiB

Open AccessArticle

Optimal Array Design and Directive Sensors for Guided Waves DoA Estimation

by Marco Dibiase, Masoud Mohammadgholiha and Luca De Marchi

Sensors 2022, 22(3), 780; https://0-doi-org.brum.beds.ac.uk/10.3390/s22030780 - 20 Jan 2022

Cited by 10 | Viewed by 1699

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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19 pages, 4485 KiB

Open AccessArticle

Towards Interpretable Machine Learning for Automated Damage Detection Based on Ultrasonic Guided Waves

by Christopher Schnur, Payman Goodarzi, Yevgeniya Lugovtsova, Jannis Bulling, Jens Prager, Kilian Tschöke, Jochen Moll, Andreas Schütze and Tizian Schneider

Sensors 2022, 22(1), 406; https://0-doi-org.brum.beds.ac.uk/10.3390/s22010406 - 05 Jan 2022

Cited by 9 | Viewed by 3659

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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23 pages, 8627 KiB

Open AccessArticle

Experimental Detection and Measurement of Crack-Type Damage Features in Composite Thin-Wall Beams Using Modal Analysis

by Josué Pacheco-Chérrez, Diego Cárdenas and Oliver Probst

Sensors 2021, 21(23), 8102; https://0-doi-org.brum.beds.ac.uk/10.3390/s21238102 - 03 Dec 2021

Cited by 7 | Viewed by 1763

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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20 pages, 6945 KiB

Open AccessArticle

Multiple Damage Detection of an Offshore Helideck through the Two-Step Artificial Neural Network Based on the Limited Mode Shape Data

by Byungmo Kim, Chanyeong Kim and Seung-Hyun Ha

Sensors 2021, 21(21), 7357; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217357 - 05 Nov 2021

Cited by 2 | Viewed by 1711

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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17 pages, 75866 KiB

Open AccessArticle

Smart Inlays for Simultaneous Crack Sensing and Arrest in Multifunctional Bondlines of Composites

by Chresten von der Heide, Julian Steinmetz, Martin J. Schollerer, Christian Hühne, Michael Sinapius and Andreas Dietzel

Sensors 2021, 21(11), 3852; https://0-doi-org.brum.beds.ac.uk/10.3390/s21113852 - 02 Jun 2021

Cited by 4 | Viewed by 2941

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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25 pages, 5391 KiB

Open AccessArticle

Lamb Wave Scattering Analysis for Interface Damage Detection between a Surface-Mounted Block and Elastic Plate

by Mikhail V. Golub, Alisa N. Shpak, Inka Mueller, Sergey I. Fomenko and Claus-Peter Fritzen

Sensors 2021, 21(3), 860; https://0-doi-org.brum.beds.ac.uk/10.3390/s21030860 - 28 Jan 2021

Cited by 13 | Viewed by 2091

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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17 pages, 6059 KiB

Open AccessArticle

Detecting of the Crack and Leakage in the Joint of Precast Concrete Segmental Bridge Using Piezoceramic Based Smart Aggregate

by Jianqun Wang and Zhe Fan

Sensors 2020, 20(18), 5398; https://0-doi-org.brum.beds.ac.uk/10.3390/s20185398 - 21 Sep 2020

Cited by 3 | Viewed by 2884

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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16 pages, 6976 KiB

Open AccessEditor’s ChoiceArticle

A Variable Data Fusion Approach for Electromechanical Impedance-Based Damage Detection

by Shishir Kumar Singh, Rohan Soman, Tomasz Wandowski and Pawel Malinowski

Sensors 2020, 20(15), 4204; https://0-doi-org.brum.beds.ac.uk/10.3390/s20154204 - 28 Jul 2020

Cited by 19 | Viewed by 2983

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

Jump to: Research, Other

22 pages, 12872 KiB

Open AccessReview

A Review on Low-Cost Microwave Doppler Radar Systems for Structural Health Monitoring

by Davi V. Q. Rodrigues and Changzhi Li

Sensors 2021, 21(8), 2612; https://0-doi-org.brum.beds.ac.uk/10.3390/s21082612 - 08 Apr 2021

Cited by 11 | Viewed by 4422

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

Jump to: Research, Review

10 pages, 6683 KiB

Open AccessLetter

Detection of Pin Failure in Carbon Fiber Composites Using the Electro-Mechanical Impedance Method

by Jochen Moll, Matthias Schmidt, Johannes Käsgen, Jörg Mehldau, Marcel Bücker and Felix Haupt

Sensors 2020, 20(13), 3732; https://0-doi-org.brum.beds.ac.uk/10.3390/s20133732 - 03 Jul 2020

Cited by 3 | Viewed by 2385

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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