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Editorial

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6 pages, 186 KiB

Open AccessEditorial

Sensors for Structural Health Monitoring and Condition Monitoring

by Francesc Pozo, Diego A. Tibaduiza and Yolanda Vidal

Sensors 2021, 21(5), 1558; https://doi.org/10.3390/s21051558 - 24 Feb 2021

Cited by 11 | Viewed by 4320

Abstract

Structural control and health monitoring as condition monitoring are some essential areas that allow for different system parameters to be designed, supervised, controlled, and evaluated during the system’s operation in different processes, such as those used in machinery, structures, and different physical variables [...] Read more.

Structural control and health monitoring as condition monitoring are some essential areas that allow for different system parameters to be designed, supervised, controlled, and evaluated during the system’s operation in different processes, such as those used in machinery, structures, and different physical variables in mechanical, chemical, electrical, aeronautical, civil, electronics, mechatronics, and agricultural engineering applications, among others [...] Full article

(This article belongs to the Special Issue Sensors for Structural Health Monitoring and Condition Monitoring)

Research

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25 pages, 3781 KiB

Open AccessArticle

Analytical and Experimental Study of Fatigue-Crack-Growth AE Signals in Thin Sheet Metals

by Roshan Joseph and Victor Giurgiutiu

Sensors 2020, 20(20), 5835; https://doi.org/10.3390/s20205835 - 15 Oct 2020

Cited by 19 | Viewed by 2762

Abstract

The acoustic emission (AE) method is a very popular and well-developed method for passive structural health monitoring of metallic and composite structures. AE method has been efficiently used for damage source detection and damage characterization in a large variety of structures over the [...] Read more.

The acoustic emission (AE) method is a very popular and well-developed method for passive structural health monitoring of metallic and composite structures. AE method has been efficiently used for damage source detection and damage characterization in a large variety of structures over the years, such as thin sheet metals. Piezoelectric wafer active sensors (PWASs) are lightweight and inexpensive transducers, which recently drew the attention of the AE research community for AE sensing. The focus of this paper is on understanding the fatigue crack growth AE signals in thin sheet metals recorded using PWAS sensors on the basis of the Lamb wave theory and using this understanding for predictive modeling of AE signals. After a brief introduction, the paper discusses the principles of sensing acoustic signals by using PWAS. The derivation of a closed-form expression for PWAS response due to a stress wave is presented. The transformations happening to the AE signal according to the instrumentations we used for the fatigue crack AE experiment is also discussed. It is followed by a summary of the in situ AE experiments performed for recording fatigue crack growth AE and the results. Then, we present an analytical model of fatigue crack growth AE and a comparison with experimental results. The fatigue crack growth AE source was modeled analytically using the dipole moment concept. By using the source modeling concept, the analytical predictive modeling and simulation of the AE were performed using normal mode expansion (NME). The simulation results showed good agreement with experimental results. A strong presence of nondispersive S0 Lamb wave mode due to the fatigue crack growth event was observed in the simulation and experiment. Finally, the analytical method was verified using the finite element method. The paper ends with a summary and conclusions; suggestions for further work are also presented. Full article

(This article belongs to the Special Issue Sensors for Structural Health Monitoring and Condition Monitoring)

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21 pages, 15886 KiB

Open AccessArticle

Moving Accelerometers to the Tip: Monitoring of Wind Turbine Blade Bending Using 3D Accelerometers and Model-Based Bending Shapes

by Theresa Loss and Alexander Bergmann

Sensors 2020, 20(18), 5337; https://doi.org/10.3390/s20185337 - 17 Sep 2020

Cited by 7 | Viewed by 3674

Abstract

Increasing the length of wind turbine blades for maximum energy capture leads to larger loads and forces acting on the blades. In particular, alternate bending due to gravity or nonuniform wind profiles leads to increased loads and imminent fatigue. Therefore, blade monitoring in [...] Read more.

Increasing the length of wind turbine blades for maximum energy capture leads to larger loads and forces acting on the blades. In particular, alternate bending due to gravity or nonuniform wind profiles leads to increased loads and imminent fatigue. Therefore, blade monitoring in operation is needed to optimise turbine settings and, consequently, to reduce alternate bending. In our approach, an acceleration model was used to analyse periodically occurring deviations from uniform bending. By using hierarchical clustering, significant bending patterns could be extracted and patterns were analysed with regard to reference data. In a simulation of alternate bending effects, various effects were successfully represented by different bending patterns. A real data experiment with accelerometers mounted at the blade tip of turbine blades demonstrated a clear relation between the rotation frequency and the resulting bending patterns. Additionally, the markedness of bending shapes could be used to assess the amount of alternate bending of the blade in both simulations and experiments. The results demonstrate that model-based bending shapes provide a strong indication for alternate bending and, consequently, can be used to optimise turbine settings. Full article

(This article belongs to the Special Issue Sensors for Structural Health Monitoring and Condition Monitoring)

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20 pages, 11538 KiB

Open AccessArticle

Digital Filtering of Railway Track Coordinates in Mobile Multi–Receiver GNSS Measurements

by Andrzej Wilk, Wladyslaw Koc, Cezary Specht, Slawomir Judek, Krzysztof Karwowski, Piotr Chrostowski, Krzysztof Czaplewski, Pawel S. Dabrowski, Sławomir Grulkowski, Roksana Licow, Jacek Skibicki, Mariusz Specht and Jacek Szmaglinski

Sensors 2020, 20(18), 5018; https://doi.org/10.3390/s20185018 - 4 Sep 2020

Cited by 16 | Viewed by 3023

Abstract

The article discusses an important issue in connection with the technique of mobile Global Navigation Satellite System (GNSS) measurements of railway track coordinates, which is digital filtering performed to precisely determine railway track axes. For this purpose, a measuring technique is proposed which [...] Read more.

The article discusses an important issue in connection with the technique of mobile Global Navigation Satellite System (GNSS) measurements of railway track coordinates, which is digital filtering performed to precisely determine railway track axes. For this purpose, a measuring technique is proposed which bases on the use of a measuring platform with a number of appropriately distributed GNSS receivers, where two of them determine the directional base vector of the platform. The receivers used in the research had high measuring frequency in the Real Time Kinematic (RTK) operating mode and enabled correction of the obtained results in post–processing. A key problem discussed in the article is the method for assessing the quality of the measurement results obtained from GNSS receivers, and their preparation for further processing making use of geometrically constrained parameters of the base vector and specialized digital filtering, among other elements, to precisely determining the track axis. The obtained results confirm the applicability of the used method of GNSS signal processing. Full article

(This article belongs to the Special Issue Sensors for Structural Health Monitoring and Condition Monitoring)

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20 pages, 3951 KiB

Open AccessArticle

Nonlinear Feature Extraction Through Manifold Learning in an Electronic Tongue Classification Task

by Jersson X. Leon-Medina, Maribel Anaya, Francesc Pozo and Diego Tibaduiza

Sensors 2020, 20(17), 4834; https://doi.org/10.3390/s20174834 - 27 Aug 2020

Cited by 26 | Viewed by 4754

Abstract

A nonlinear feature extraction-based approach using manifold learning algorithms is developed in order to improve the classification accuracy in an electronic tongue sensor array. The developed signal processing methodology is composed of four stages: data unfolding, scaling, feature extraction, and classification. This study [...] Read more.

A nonlinear feature extraction-based approach using manifold learning algorithms is developed in order to improve the classification accuracy in an electronic tongue sensor array. The developed signal processing methodology is composed of four stages: data unfolding, scaling, feature extraction, and classification. This study aims to compare seven manifold learning algorithms: Isomap, Laplacian Eigenmaps, Locally Linear Embedding (LLE), modified LLE, Hessian LLE, Local Tangent Space Alignment (LTSA), and t-Distributed Stochastic Neighbor Embedding (t-SNE) to find the best classification accuracy in a multifrequency large-amplitude pulse voltammetry electronic tongue. A sensitivity study of the parameters of each manifold learning algorithm is also included. A data set of seven different aqueous matrices is used to validate the proposed data processing methodology. A leave-one-out cross validation was employed in 63 samples. The best accuracy (

96.83 %

) was obtained when the methodology uses Mean-Centered Group Scaling (MCGS) for data normalization, the t-SNE algorithm for feature extraction, and k-nearest neighbors (kNN) as classifier. Full article

(This article belongs to the Special Issue Sensors for Structural Health Monitoring and Condition Monitoring)

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18 pages, 5475 KiB

Open AccessArticle

Miniature Resistance Measurement Device for Structural Health Monitoring of Reinforced Concrete Infrastructure

by Dean M. Corva, Seyyed Sobhan Hosseini, Frank Collins, Scott D. Adams, Will P. Gates and Abbas Z. Kouzani

Sensors 2020, 20(15), 4313; https://doi.org/10.3390/s20154313 - 2 Aug 2020

Cited by 20 | Viewed by 4211

Abstract

A vast amount of civil infrastructure is constructed using reinforced concrete, which can be susceptible to corrosion, posing significant risks. Corrosion of reinforced concrete has various causes, with chloride ingress known to be a major contributor. Monitoring this chloride ingress would allow for [...] Read more.

A vast amount of civil infrastructure is constructed using reinforced concrete, which can be susceptible to corrosion, posing significant risks. Corrosion of reinforced concrete has various causes, with chloride ingress known to be a major contributor. Monitoring this chloride ingress would allow for preventative maintenance to be less intrusive at a lower cost. Currently, chloride sensing methods are bulky and expensive, leaving the majority of concrete infrastructures unmonitored. This paper presents the design and fabrication of a miniature, low-cost device that can be embedded into concrete at various locations and depths. The device measures localized concrete resistance, correlating to the chloride ingress in the concrete using equations listed in this paper, and calculated results from two experiments are presented. The device benefits from a four-probe architecture, injecting a fixed frequency AC waveform across its outer electrodes within the cement block. Voltage across the internal electrodes is measured with a microcontroller and converted to a resistance value, communicated serially to an external computer. A final test showcases the ability of the device for three-dimensional mass deployment. Full article

(This article belongs to the Special Issue Sensors for Structural Health Monitoring and Condition Monitoring)

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13 pages, 6434 KiB

Open AccessArticle

Condition Evaluation of an Existing T-Beam Bridge Based on Neutral Axis Variation Monitored with Ultrasonic Coda Waves in a Network of Sensors

by Hanyu Zhan, Hanwan Jiang, Jinquan Zhang and Ruinian Jiang

Sensors 2020, 20(14), 3895; https://doi.org/10.3390/s20143895 - 13 Jul 2020

Cited by 7 | Viewed by 2456

Abstract

Neutral axis passing through the stiffness centroid of a structure is correlated with structural health conditions. Traditional techniques rely on gauge arrays to observe strains at their installation positions, and then locate a neutral axis through the intercept of the strain diagram. However, [...] Read more.

Neutral axis passing through the stiffness centroid of a structure is correlated with structural health conditions. Traditional techniques rely on gauge arrays to observe strains at their installation positions, and then locate a neutral axis through the intercept of the strain diagram. However, these localization results will be severely deviated if any damages exist among gauges or inside structures. In this paper, a novel technique is proposed to locate the neutral axis by measuring and analyzing ultrasonic coda waves in a network of transducers. Because of multiple trajectories, coda waves are sensitive to minor changes in a large volume of media that are not limited to direct paths between sensors. This technique is not only capable of locating a neutral axis with great efficiency and accuracy, but can also indicate global structural health and inner damages. The applicability of the technique is demonstrated by monitoring a 30 m concrete T-beam subjected to four-point loading tests. With an array of transducers placed at the surface, the neutral axes in the large region are located. The localization results also show clear trends that the global neutral axis moves up as the loads increase, which indicates the beam contains certain degrees of inner damage. Full article

(This article belongs to the Special Issue Sensors for Structural Health Monitoring and Condition Monitoring)

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18 pages, 6873 KiB

Open AccessArticle

Performance Degradation Assessment of Concrete Beams Based on Acoustic Emission Burst Features and Mahalanobis—Taguchi System

by Md Arafat Habib, Akhand Rai and Jong-Myon Kim

Sensors 2020, 20(12), 3402; https://doi.org/10.3390/s20123402 - 16 Jun 2020

Cited by 8 | Viewed by 2699

Abstract

Acoustic emission (AE) has been used extensively for structural health monitoring based on the stress waves generated due to evolution of cracks in concrete structures. A major concern while using AE features is that each of them responds differently to the fractures in [...] Read more.

Acoustic emission (AE) has been used extensively for structural health monitoring based on the stress waves generated due to evolution of cracks in concrete structures. A major concern while using AE features is that each of them responds differently to the fractures in concrete structures. To tackle this problem, Mahalanobis—Taguchi system (MTS) is utilized, which fuses the AE feature space to provide comprehensive and reliable degradation indicator with a feature selection method to determine useful features. Further, majority of the existing investigations gave little attention to naturally occurring cracks, which are actually more difficult to detect. In this study, a novel degradation indicator (DI) based on AE features and MTS is proposed to indicate the performance degradation in reinforced concrete beams. The experimental results confirm that the MTS can successfully distinguish between healthy and faulty conditions. To alleviate the noise from the DI obtained through MTS, a noise-removal strategy based on Chebyshev inequality is suggested. The results show that the proposed DI based on AE features and MTS is capable of detecting early stage cracks as well as development of damage in concrete beams. Full article

(This article belongs to the Special Issue Sensors for Structural Health Monitoring and Condition Monitoring)

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13 pages, 4888 KiB

Open AccessArticle

Monitoring of Processing Conditions of an Ultrasonic Vibration-Assisted Ball-Burnishing Process

by Aida Estevez-Urra, Jordi Llumà, Ramón Jerez-Mesa and Jose Antonio Travieso-Rodriguez

Sensors 2020, 20(9), 2562; https://doi.org/10.3390/s20092562 - 30 Apr 2020

Cited by 16 | Viewed by 3560

Abstract

Although numerous references present the beneficial effects on surface integrity of ultrasonic vibration-assisted ball burnishing (UVABB), nothing has been reported about the dynamic behavior of the UVABB tool, workpiece, and machine triad during the process. In this paper, a dynamic monitorization through a [...] Read more.

Although numerous references present the beneficial effects on surface integrity of ultrasonic vibration-assisted ball burnishing (UVABB), nothing has been reported about the dynamic behavior of the UVABB tool, workpiece, and machine triad during the process. In this paper, a dynamic monitorization through a set of 5 accelerometers is tested to analyze the interactions between the tool–workpiece–machine mechanical assembly. A UVABB tool attached to a milling machine and equipped with a piezoelectric stack that is able to assist the process with a 40-kHz vibration is tested on a milled C45 steel surface. First, the natural frequencies of the mechanical system are obtained through hammer impact tests. Then, the vibratory signals transmitted during the execution of the process are monitored and compared to those: two feed velocities and two burnishing preloads, all with and without vibration-assistance. Results show that the proposed accelerometer set is valid to assess the behavior of a UVABB process. The system’s natural frequencies are not varied by vibration-assistance and are not excited when the piezoelectric is functioning. It is confirmed that UVABB is safe for the machine and the tool, and there is no unexpected excited frequencies due to the piezoelectric excitation. Full article

(This article belongs to the Special Issue Sensors for Structural Health Monitoring and Condition Monitoring)

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15 pages, 15186 KiB

Open AccessArticle

Direct Use of the Savitzky–Golay Filter to Develop an Output-Only Trend Line-Based Damage Detection Method

by Hadi Kordestani and Chunwei Zhang

Sensors 2020, 20(7), 1983; https://doi.org/10.3390/s20071983 - 2 Apr 2020

Cited by 71 | Viewed by 4360

Abstract

The Savitzky–Golay filter (SGF) is a time-domain technique that determines a trend line for a signal. The direct application of SGF for damage localization and quantification is investigated in this paper. Therefore, a single-stage trend line-based damage detection method employing SGF is proposed [...] Read more.

The Savitzky–Golay filter (SGF) is a time-domain technique that determines a trend line for a signal. The direct application of SGF for damage localization and quantification is investigated in this paper. Therefore, a single-stage trend line-based damage detection method employing SGF is proposed in which the damage is located and quantified at the bridge under moving load. A simply supported beam under moving sprung mass is numerically simulated to verify the proposed method. Four different velocities and five different single- and multi-damage scenarios are considered. The acceleration data along the beam are obtained, manually polluted with noise and their trend lines are then determined using SGF. The results show that the proposed method can accurately locate and quantify the damage using these trend lines. It is proved that the proposed method is insensitive to the noise and velocity variation in which having a constant velocity is a hard task before and after damage. Additionally, defining a normalization factor and fitting a Gaussian curve to this factor provide an estimation for the baseline and therefore, it categorizes the proposed method as baseline-free method. Full article

(This article belongs to the Special Issue Sensors for Structural Health Monitoring and Condition Monitoring)

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19 pages, 2003 KiB

Open AccessArticle

Vibration Anatomy and Damage Detection in Power Transmission Towers with Limited Sensors

by R. Karami-Mohammadi, M. Mirtaheri, M. Salkhordeh and M. A. Hariri-Ardebili

Sensors 2020, 20(6), 1731; https://doi.org/10.3390/s20061731 - 20 Mar 2020

Cited by 21 | Viewed by 4097

Abstract

This study presents a technique to identify the vibration characteristics in power transmission towers and to detect the potential structural damages. This method is based on the curvature of the mode shapes coupled with a continuous wavelet transform. The elaborated numerical method is [...] Read more.

This study presents a technique to identify the vibration characteristics in power transmission towers and to detect the potential structural damages. This method is based on the curvature of the mode shapes coupled with a continuous wavelet transform. The elaborated numerical method is based on signal processing of the output that resulted from ambient vibration. This technique benefits from a limited number of sensors, which makes it a cost-effective approach compared to others. The optimal spatial location for these sensors is obtained by the minimization of the non-diagonal entries in the modal assurance criterion (MAC) matrix. The Hilbert–Huang transform was also used to identify the dynamic anatomy of the structure. In order to simulate the realistic condition of the measured structural response in the field condition, a 10% noise is added to the response of the numerical model. Four damage scenarios were considered, and the potential damages were identified using wavelet transform on the difference of mode shapes curvature in the intact and damaged towers. Results show a promising accuracy considering the small number of applied sensors. This study proposes a low-cost and feasible technique for structural health monitoring. Full article

(This article belongs to the Special Issue Sensors for Structural Health Monitoring and Condition Monitoring)

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23 pages, 9641 KiB

Open AccessArticle

Bridge Damage Identification Using Vehicle Bump Based on Additional Virtual Masses

by Qingxia Zhang, Jilin Hou and Łukasz Jankowski

Sensors 2020, 20(2), 394; https://doi.org/10.3390/s20020394 - 10 Jan 2020

Cited by 8 | Viewed by 3072

Abstract

Structural damage identification plays an important role in providing effective evidence for the health monitoring of bridges in service. Due to the limitations of measurement points and lack of valid structural response data, the accurate identification of structural damage, especially for large-scale structures, [...] Read more.

Structural damage identification plays an important role in providing effective evidence for the health monitoring of bridges in service. Due to the limitations of measurement points and lack of valid structural response data, the accurate identification of structural damage, especially for large-scale structures, remains difficult. Based on additional virtual mass, this paper presents a damage identification method for bridges using a vehicle bump as the excitation. First, general equations of virtual modifications, including virtual mass, stiffness, and damping, are derived. A theoretical method for damage identification, which is based on additional virtual mass, is formulated. The vehicle bump is analyzed, and the bump-induced excitation is estimated via a detailed analysis in four periods: separation, free-fall, contact, and coupled vibrations. The precise estimation of bump-induced excitation is then applied to a bridge. This allows the additional virtual mass method to be used, which requires knowledge of the excitations and acceleration responses in order to construct the frequency responses of a virtual structure with an additional virtual mass. Via this method, a virtual mass with substantially more weight than a typical vehicle is added to the bridge, which provides a sufficient amount of modal information for accurate damage identification while avoiding the bridge overloading problem. A numerical example of a two-span continuous beam is used to verify the proposed method, where the damage can be identified even with 15% Gaussian random noise pollution using a 1-degree of freedom (DOF) car model and 4-DOF model. Full article

(This article belongs to the Special Issue Sensors for Structural Health Monitoring and Condition Monitoring)

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26 pages, 761 KiB

Open AccessArticle

A Frequency-Based Approach for the Detection and Classification of Structural Changes Using t-SNE †

by David Agis and Francesc Pozo

Sensors 2019, 19(23), 5097; https://doi.org/10.3390/s19235097 - 21 Nov 2019

Cited by 26 | Viewed by 3835

Abstract

This work presents a structural health monitoring (SHM) approach for the detection and classification of structural changes. The proposed strategy is based on t-distributed stochastic neighbor embedding (t-SNE), a nonlinear procedure that is able to represent the local structure of [...] Read more.

This work presents a structural health monitoring (SHM) approach for the detection and classification of structural changes. The proposed strategy is based on t-distributed stochastic neighbor embedding (t-SNE), a nonlinear procedure that is able to represent the local structure of high-dimensional data in a low-dimensional space. The steps of the detection and classification procedure are: (i) the data collected are scaled using mean-centered group scaling (MCGS); (ii) then principal component analysis (PCA) is applied to reduce the dimensionality of the data set; (iii) t-SNE is applied to represent the scaled and reduced data as points in a plane defining as many clusters as different structural states; and (iv) the current structure to be diagnosed will be associated with a cluster or structural state based on three strategies: (a) the smallest point-centroid distance; (b) majority voting; and (c) the sum of the inverse distances. The combination of PCA and t-SNE improves the quality of the clusters related to the structural states. The method is evaluated using experimental data from an aluminum plate with four piezoelectric transducers (PZTs). Results are illustrated in frequency domain, and they manifest the high classification accuracy and the strong performance of this method. Full article

(This article belongs to the Special Issue Sensors for Structural Health Monitoring and Condition Monitoring)

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13 pages, 5830 KiB

Open AccessArticle

Microstrip Patch Strain Sensor Miniaturization Using Sierpinski Curve Fractal Geometry

by Michal Herbko and Przemyslaw Lopato

Sensors 2019, 19(18), 3989; https://doi.org/10.3390/s19183989 - 15 Sep 2019

Cited by 13 | Viewed by 3865

Abstract

In this paper miniaturization of a microstrip patch strain sensor (MPSS) using fractal geometry was proposed and analyzed. For this purpose, the transducer of Sierpinski curve geometry was utilized and compared with the most commonly utilized rectangular resonator-based one. Both sensors were designed [...] Read more.

In this paper miniaturization of a microstrip patch strain sensor (MPSS) using fractal geometry was proposed and analyzed. For this purpose, the transducer of Sierpinski curve geometry was utilized and compared with the most commonly utilized rectangular resonator-based one. Both sensors were designed for the same resonant frequency value (2.725 GHz). This fact allows analysis of the influence of the patch (resonator) shape and size on the resonant frequency shift. This is very important as the sensors with the same resonator shape but designed on various operating frequencies have various resonant frequency shifts. Simulation and experimental analysis for all sensors were carried out. A good convergence between results of simulation and measurements was achieved. The obtained results proved the possibility of microstrip strain sensor dimensions reduction using Sierpinski curve fractal geometry. Additionally, an influence of microstrip line deformation for proposed sensors was studied. Full article

(This article belongs to the Special Issue Sensors for Structural Health Monitoring and Condition Monitoring)

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13 pages, 10902 KiB

Open AccessArticle

On the Application of Laser Vibrometry to Perform Structural Health Monitoring in Non-Stationary Conditions of a Hydropower Dam

by Mateja Klun, Dejan Zupan, Jože Lopatič and Andrej Kryžanowski

Sensors 2019, 19(17), 3811; https://doi.org/10.3390/s19173811 - 3 Sep 2019

Cited by 22 | Viewed by 4998

Abstract

This paper presents the first application of the Laser Doppler Vibrometer (LDV) in non-stationary conditions within a hydropower plant powerhouse. The aim of this research is to develop a methodology to include non-contact vibration monitoring as part of structural health monitoring of concrete [...] Read more.

This paper presents the first application of the Laser Doppler Vibrometer (LDV) in non-stationary conditions within a hydropower plant powerhouse. The aim of this research is to develop a methodology to include non-contact vibration monitoring as part of structural health monitoring of concrete dams. We have performed in-situ structural vibration measurements on the run-of-the-river Brežice dam in Slovenia during the start-up tests and regular operation. In recent decades, the rapid development of laser measurement technology has provided powerful methods for a variety of measuring tasks. Despite these recent developments, the use of lasers for measuring has been limited to sites provided with stationary conditions. This paper explains the elimination of pseudo-vibration and measurement noise inherent in the non-stationary conditions of the site. Upon removal of the noise, fatigue of the different structural elements of the powerhouse could be identified if significant changes over time are observed in the eigenfrequencies. The use of laser technology is to complement the regular monitoring activities on large dams, since observation and analysis of integrity parameters provide indispensable information for decision making and maintaining good structural health of ageing dams. Full article

(This article belongs to the Special Issue Sensors for Structural Health Monitoring and Condition Monitoring)

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16 pages, 4700 KiB

Open AccessArticle

Influence of Volumetric Damage Parameters on Patch Antenna Sensor-Based Damage Detection of Metallic Structure

by Zhiping Liu, Hanjin Yu, Kai Zhou, Runfa Li and Qian Guo

Sensors 2019, 19(14), 3232; https://doi.org/10.3390/s19143232 - 23 Jul 2019

Cited by 6 | Viewed by 4037

Abstract

Antenna sensors have been employed for crack monitoring of metallic materials. Existing studies have mainly focused on the mathematical relationship between the surface crack length of metallic material and the resonant frequency. The influence of the crack depth on the sensor output and [...] Read more.

Antenna sensors have been employed for crack monitoring of metallic materials. Existing studies have mainly focused on the mathematical relationship between the surface crack length of metallic material and the resonant frequency. The influence of the crack depth on the sensor output and the difference of whether the crack is depth-penetrated remains unexplored. Therefore, in this work, a numerical simulation method was used to investigate the current density distribution characteristics of the ground plane (metallic material) with different crack geometric parameters. The data reveals that, compared with the crack length, the crack depth has a greater influence on the resonant frequency. The relationship between the frequency and the crack geometric parameters was discussed by characterizing the current density and sensor output under different crack lengths and depths. Therefore, the feasibility of monitoring another common damage of metallic materials, i.e., corrosion pit, was explored. Furthermore, the influences of crack and corrosion pit geometric parameters on the output results were validated by experiments. Full article

(This article belongs to the Special Issue Sensors for Structural Health Monitoring and Condition Monitoring)

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24 pages, 5133 KiB

Open AccessArticle

Structural Health Monitoring with Sensor Data and Cosine Similarity for Multi-Damages

by Byungmo Kim, Cheonhong Min, Hyungwoo Kim, Sugil Cho, Jaewon Oh, Seung-Hyun Ha and Jin-hak Yi

Sensors 2019, 19(14), 3047; https://doi.org/10.3390/s19143047 - 10 Jul 2019

Cited by 21 | Viewed by 4818

Abstract

There is a large risk of damage, triggered by harsh ocean environments, associated with offshore structures, so structural health monitoring plays an important role in preventing the occurrence of critical and global structural failure from such damage. However, obstacles, such as applicability in [...] Read more.

There is a large risk of damage, triggered by harsh ocean environments, associated with offshore structures, so structural health monitoring plays an important role in preventing the occurrence of critical and global structural failure from such damage. However, obstacles, such as applicability in the field and increasing calculation costs with increasing structural complexity, remain for real-time structure monitoring offshore. Therefore, this study proposes the comparison of cosine similarity with sensor data to overcome such challenges. As the comparison target, this method uses the rate of changes of natural frequencies before and after the occurrence of various damage scenarios, including not only single but multiple damages, which are organized by the experiment technique design. The comparison method alerts to the occurrence of damage using a normalized warning index, which enables workers to manage the risk of damage. By comparison, moreover, the case most similar with the current status is directly figured out without any additional analysis between monitoring and damage identification, which renders the damage identification process simpler. Plus, the averaged rate of errors in detection is suggested to evaluate the damage level more precisely, if needed. Therefore, this method contributes to the application of real-time structural health monitoring for offshore structures by providing an approach to improve the usability of the proposed technique. Full article

(This article belongs to the Special Issue Sensors for Structural Health Monitoring and Condition Monitoring)

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23 pages, 6851 KiB

Open AccessArticle

Modeling, Simulation, Experimentation, and Compensation of Temperature Effect in Impedance-Based SHM Systems Applied to Steel Pipes

by Rothschild A. Antunes, Nicolás E. Cortez, Bárbara M. Gianesini and Jozue Vieira Filho

Sensors 2019, 19(12), 2802; https://doi.org/10.3390/s19122802 - 22 Jun 2019

Cited by 24 | Viewed by 5319

Abstract

Pipelines have been widely used for the transportation of chemical products, mainly those related to the petroleum industry. Damage in such pipelines can produce leakage with unpredictable consequences to the environment. There are different structural health monitoring (SHM) systems such as Lamb wave, [...] Read more.

Pipelines have been widely used for the transportation of chemical products, mainly those related to the petroleum industry. Damage in such pipelines can produce leakage with unpredictable consequences to the environment. There are different structural health monitoring (SHM) systems such as Lamb wave, comparative vacuum, acoustic emission, etc. for monitoring such structures. However, those based on piezoelectric sensors and electromechanical impedance technique (EMI) measurements are simple and efficient, and have been applied in a wide range of structures, including pipes. A disadvantage of such technique is that temperature changes can lead to false diagnoses. To overcome this disadvantage, temperature variation compensation techniques are normally incorporated. Therefore, this work has developed a complete study applied to damage detection in pipelines, including an innovative technique for compensating the temperature effect in EMI-based SHM and the modeling of piezoceramics bonded to pipeline structures using finite elements. Experimental results were used to validate the model. Moreover, the compensation method was tested in two steel pipes—healthy and damaged—compensating the temperature effect ranging from −40 °C to +80 °C, with analysis on the frequency range from 5 kHz to 120 kHz. The simulated and experimental results showed that the studies effectively contribute to the SHM area, mainly to EMI-based techniques. Full article

(This article belongs to the Special Issue Sensors for Structural Health Monitoring and Condition Monitoring)

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Review

Jump to: Editorial, Research, Other

32 pages, 2728 KiB

Open AccessReview

Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications

by Diego A. Tibaduiza Burgos, Ricardo C. Gomez Vargas, Cesar Pedraza, David Agis and Francesc Pozo

Sensors 2020, 20(3), 733; https://doi.org/10.3390/s20030733 - 29 Jan 2020

Cited by 87 | Viewed by 10894

Abstract

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using [...] Read more.

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification. Full article

(This article belongs to the Special Issue Sensors for Structural Health Monitoring and Condition Monitoring)

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20 pages, 5895 KiB

Open AccessLetter

Damage Detection of Bridges under Environmental Temperature Changes Using a Hybrid Method

by Xiang Wang, Qingfei Gao and Yang Liu

Sensors 2020, 20(14), 3999; https://doi.org/10.3390/s20143999 - 18 Jul 2020

Cited by 21 | Viewed by 3094

Abstract

Principal component analysis (PCA)-based method is popular for detecting the damage of bridges under varying environmental temperatures. However, this method deletes some information when the damage features are projected in the direction of nonprincipal components; thus, the effectiveness of PCA-based methods will decrease [...] Read more.

Principal component analysis (PCA)-based method is popular for detecting the damage of bridges under varying environmental temperatures. However, this method deletes some information when the damage features are projected in the direction of nonprincipal components; thus, the effectiveness of PCA-based methods will decrease if the deleted information is related to bridge damage. To address this issue, a hybrid method is proposed to detect the damage of bridges under environmental temperature changes. On one side, the PCA-based method is applied to deal with the nonprincipal components; on the other side, the Gaussian mixture method (GMM) is used to classify all the principal components into different clusters, and then the novel detection method is implemented to detect bridge damage for each cluster. In this way, all the damage feature information is saved and used to detect bridge damage. The numerical example and example of an actual bridge show that the proposed hybrid method is effective in detecting bridge damage under environmental temperature changes. The GMM is effective for classifying the natural monitoring frequency data of actual bridges, and the relationship between the natural frequencies of actual bridges and the environmental temperature is not always linear. Full article

(This article belongs to the Special Issue Sensors for Structural Health Monitoring and Condition Monitoring)

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