Effective Structural Health Monitoring Research Articles

Generalized Shannon entropy sparse wavelet packet transform for fault detection of traction motor bearings in high-speed trains

An effective structural health monitoring method of traction motor bearings is a powerful guarantee for the safety operation of high-speed trains. However, it is exceptionally difficult to detect bearing fault characteristics from the vibration signals of traction motor bearings operating at high rotational speeds. In this scenario, a generalized Shannon entropy sparse wavelet packet transform (GSWPT) for fault detection of motor bearings is proposed in this paper. Firstly, a generalized Shannon entropy sparse regularization method is proposed to obtain sparse wavelet reconstruction coefficients by extending the definition of the Shannon information entropy, and the non-convex sparse regularization function is minimized by synergistic swarm optimization algorithm. Then, the wavelet node coefficients are weighted according to the second-order cyclostationarity index of the wavelet packet node to further enhance the sparsity of the reconstructed signal. Moreover, the optimal decomposition level of GSWPT is adaptively selected by the maximum sparsity and cyclostationarity criterion. Particularly, in order to verify the bearing fault detection performance of GSWPT in practical engineering, a bearing fault dynamic model of traction motor in high-speed train was established based on Hertz contact theory and the fourth-order Runge-Kutta method to obtain simulated data under strong Gaussian white noise, and a corresponding test platform was constructed to collect experimental data under different operating conditions. Finally, the applications on the simulated and experimental signals of traction motor bearings in high-speed trains demonstrate that GSWPT significantly outperforms the conventional wavelet packet transform, dual-tree complex wavelet packet transform, blind deconvolution, modal decomposition, and Infogram methods to some extent for fault detection.

Barely visible impact damage detection in CFRP composite using vibrothermography with narrowband sweep excitation

The extensive use of carbon fiber-reinforced plastic (CFRP) composite materials in various industries has posed new challenges for conducting effective structural health monitoring and nondestructive evaluation techniques. Based on local defect resonance (LDR), the vibrothermography with narrowband sweep excitation is proposed to detect BVIDs in the CFRP composite. The response spectrum of displacement at the impact crater was collected by a laser Doppler vibrometer, which was used to determine the frequency range of narrowband sweep excitation. The frequency range was further validated by the analysis of local defect resonance for thermal responses, and the sweep duration was also optimized. Subsequently, the proposed vibrothermography was employed to detect BVIDs with varying severity. The impact damage could be efficiently activated and a significant temperature rise was generated in the defective areas, which enhances the temperature contrast of the thermal images and facilitates the quantification of defects.

Curvature Correction for Crack Depth Measurement Using Ultrasonic Pulse Velocity

This study investigates the application of Ultrasonic Pulse Velocity (UPV) for crack depth estimation in cylindrical structures, focusing on two approaches: reference measurement and dual measurement. It addresses the challenge of applying UPV to curved surfaces, a scenario less studied than that of flat surfaces. The paper details the modification of UPV methodologies to account for curvature, presenting analytic solutions and numerical validations for both approaches. The findings reveal that curvature-adjusted equations yield accurate crack depth estimations, enhancing the reliability of UPV in diverse structural contexts. The study contributes to safer and more effective structural health monitoring, particularly in cylindrical infrastructures like columns and foundations.

Development and experimental validation of a prototype system for Machine Learning based SHM in composite aerostructures

The development and experimental validation of a Machine Learning (ML) vibration based Structural Health Monitoring (SHM) prototype system for composite aerostructures operating under uncertainty is presented. Following initial training, it is intended to operate autonomously, in almost real-time, without interrupting normal operation, relying on a limited number of acceleration and strain measurements from properly selected locations on the aerostructure. It is equipped with a multi-level information fusion methodology comprising multiple sensor technologies and diagnostic algorithms for optimized SHM performance in terms of damage detection and characterization. Through its flexible software, the system provides multiple options of ML based SHM methods targeting wide applicability to various types of structures. The system is validated based on numerous experiments with healthy and damaged full-scale composite bonded spars operating under multiple uncertainty factors. Its diagnostic performance indicates that the current progress in hardware and robust vibration-based Machine Learning algorithms may lead to compact, effective, and low cost SHM systems for composite aerostructures under normal operating conditions.

Computer Vision-Based Real-Time Identification of Vehicle Loads for Structural Health Monitoring of Bridges

Coupling effects of various loading conditions can cause deflections, settlements and even failure of in-service bridges. Although it is one of the most critical loads, unfortunately, loading conditions of moving vehicles are difficult to capture in real time by bridge monitoring systems currently in place for sustainable operation. To fully understand the status of a bridge, it is essential to obtain instantaneous vehicle load distributions in a dynamic traffic environment. Although there are some methods that can identify overweight vehicles, the captured vehicle-related information is scattered and incomplete and thus cannot support effective bridge structural health monitoring (BSHM). This study proposes a noncontact, vision-based approach to identification of vehicle loads for real-time monitoring of bridge structural health. The proposed method consists of four major steps: (1) establish a dual-object detection model for vehicles using YOLOv7, (2) develop a hybrid coordinate transformation model on a bridge desk, (3) develop a multiobject tracking model for real-time trajectory monitoring of moving vehicles, and (4) establish a decision-level fusion model for fusing data on vehicle loads and positions. The proposed method effectively visualizes the 3D spatiotemporal vehicular-load distribution with low delay at a speed of over 30FPS. The results show that the hybrid coordinate transformation ensures that the vehicle position error is within 1 m, a 5-fold reduction compared with the traditional method. Wheelbase is calculated through dual-object detection and transformation and is as the primary reference for vehicle position correction. The trajectory and real-time speed of vehicles are preserved, and the smoothed speed error is under 5.7%, compared with the speed measured by sensors. The authors envision that the proposed method could constitute a new approach for conducting real-time SHM of in-service bridges.

Application of Digital Twin in Structural Health Monitoring of Civil Structures: A Systematic Literature Review Based on PRISMA

Structural Health Monitoring (SHM) systems are critical for ensuring the safety and longevity of structures. Digital twin technology has emerged as a promising tool to improve the accuracy and efficiency of SHM systems. This paper presents a systematic literature review based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) on the applications of digital twin for SHM of civil structures. And this systematic literature review examines the current trends and developments in the application of digital twin technology in SHM systems for structures. The review identified 45 relevant articles published between 2010 and 2023 that covered various applications of digital twin in SHM systems, including buildings, bridges, wind turbines, trains, and gas turbines. Key themes identified from the literature review included the integration of physical models and machine learning algo-rithms, the use of distributed sensor networks, and the importance of real-time data analysis for effective SHM. The findings suggest that digital twin technology has the potential to significantly improve the accuracy and efficiency of SHM systems, but further research is needed to develop more advanced and integrated Digital Twin-Based SHM systems for different types of civil structures.

Enhanced Strain Field Reconstruction in Ship Stiffened Panels Using Optical Fiber Sensors and the Strain Function-Inverse Finite Element Method

Accurately reconstructing the strain field within stiffened ship panels is crucial for effective structural health monitoring. This study presents a groundbreaking approach to strain field reconstruction in such panels, utilizing optical fiber sensors in conjunction with the strain function-inverse finite element method (SF-iFEM). A novel technique for solving nodal strain vectors, based on the element strain function, has been devised to improve the accuracy of strain reconstruction using the inverse finite element method (iFEM), addressing the limitations associated with traditional nodal displacement vector solutions. Moreover, the proposed method for determining the equivalent neutral layer of stiffened ship panels not only reduces the number of elements effectively but also establishes a strain function between the inner and outer surfaces of the structure. Using this function, a layout scheme for optical fiber sensors on the inner side of ship stiffened panels is provided, overcoming the symmetrical arrangement constraints of iFEM for sensor placement on both the inner and outer sides of the structure. The results demonstrate a significant improvement in strain reconstruction accuracy under bending and bending–torsion deformations compared to conventional iFEM. Consequently, the findings of this research will contribute to enhancing the engineering applicability of iFEM in ship structure health monitoring.

Sampling methods based on expected traffic-volume information for long-term rotation-based bridge SHM in resource-constrained environments

Effective bridge structural health monitoring systems offer a route to assist in the safe and economic operation of bridges, but have seen limited adoption onto the short and medium-span bridges which represent the majority of our bridge stock. This lack of adoption is in part due to the practical challenges of deploying bridge SHM systems in resource-constrained environments, especially where long-term monitoring at transport network-scales is considered. This paper tackles the problem of implementing an effective sampling method to facilitate long-term, bridge rotation-based damage detection, particularly within the confines of a resource-constrained setting. We demonstrate that leveraging readily available expected traffic-volume information enables the creation of more effective bridge SHM sampling methods by capturing more vehicle crossings in the same time. Furthermore, we introduce an empirically tuned, temperature-capable bridge finite-element model for long-term simulations, incorporating a stochastic vehicle arrival model and a feature error model to allow for the presence of any residual noise/dynamics due to imperfect filtering or inaccuracies in the feature extraction stages. This allowed us to simulate 60 years worth of bridge rotation monitoring data with varying damage severities to evaluate the impact of environmental and operational variations on our proposed expected traffic volume based sampling methods. Our results show that sampling only the peak traffic hours was the most effective for the same reduced ‘on‘ time, i.e. for an 8x reduction in ‘on‘ time only a 1.5x increase in the minimum detectable damage was observed versus a 2x increase for naive random sampling.

The temperature effects on embedded PZT signals in structural health monitoring for composite structures with different thicknesses

Ultrasonics guided waves (GWs) are established as an effective structural health monitoring technique for detecting damage in composite structures. However, for GWs to be applied reliably in service, the effect of temperature needs to be accounted for baseline damage detection approaches. Previous studies have focused on developing temperature compensation methods for surface-mounted transducers. In this paper, an extensive investigation has been carried out to develop an effective temperature compensation method for GWs generated by embedded transducers in different thickness composites. To accomplish this, Laser Doppler Vibrometer experiments were carried out to obtain wavenumbers generated by embedded transducers and to compare them against surface mounted cases. The consistent trend observation of dispersion curves between surface-mounted and embedded PZTs serves as a foundation for conducting analysis of the embedded signals with the effect of temperature. Following that, GW propagation has been studied in three quasi-isotropic composites of 2 mm, 4 mm and 9 mm thicknesses. Based on the established temperature database with measurements from − 50 °C to 70 °C, a temperature compensation methodology has been developed and validated with the temperature difference of 30 °C. It demonstrates that the temperature effect on the embedded PZTs is lower in comparison to surface mounted PZTs, and the temperature compensation of a single thickness composite is scalable to other panels thickness. This novel methodology proves beneficial in reducing the scale of the experimental campaign required, which holds significant value for actual aeronautical structures characterized by variable composite thicknesses across different sections, such as in fuselage panels or wing boxes.

Interpretation of Bridge Health Monitoring Data from Satellite InSAR Technology

This paper presents a study on applying satellite Interferometric Synthetic Aperture Radar (InSAR) technology for the remote monitoring of road bridges and interpreting the results from a structural standpoint. The motivation behind this study arises from the widespread deterioration observed in many road bridges worldwide, leading to the need for large-scale, economic, and effective structural health monitoring (SHM) techniques. While traditional contact-type sensors have cost sustainability limitations, remote sensing techniques, including satellite-based InSAR, offer interesting alternative solutions. The objective of this study is three-fold: (i) to process InSAR data specifically for road bridges in operational conditions through the Multi-Temporal InSAR technique and extract displacement time series of reflective targets on their decks; (ii) to interpret the observed millimetric bridge displacements to verify the consistency with expected response to environmental loads and the possibility to detect unexpected behaviours; and (iii) to investigate the correlation between bridge displacements and environmental loads as temperature and river water flow variations. The study focuses on the multi-span prestressed concrete A22 Po River Bridge in Italy, utilising a dataset of X-Band HIMAGE mode Stripmap images acquired over eight years by the satellite constellation COSMO-SkyMed. The study demonstrates the effectiveness of InSAR-based SHM in detecting temperature-induced displacements and identifying different bridge spans simply by studying the sign of the correlation between displacements and temperature variation. It also reveals an unexpected behaviour in a portion of the bridge retrofitted to prevent scour issues a few years before the dataset start date. Furthermore, the correlation between pier displacements and river level variations underscores the importance of considering environmental factors and the geotechnical characteristics of the foundation soils in bridge monitoring. The results obtained from this study are significant with a view to using this satellite InSAR-based monitoring for early detection of anomalous bridge behaviour on a large scale.

Experimental Study on Monitoring Damage Progression of Basalt-FRP Reinforced Concrete Slabs Using Acoustic Emission and Machine Learning.

Basalt fiber-reinforced polymer (BFRP) reinforced concrete is a new alternative to conventional steel-reinforced concrete due to its high tensile strength and corrosion resistance characteristics. However, as BFRP is a brittle material, unexpected failure of concrete structures reinforced with BFRP may occur. In this study, the damage initiation and progression of BFRP-reinforced concrete slabs were monitored using the acoustic emission (AE) method as a structural health monitoring (SHM) solution. Two simply supported slabs were instrumented with an array of AE sensors in addition to a high-resolution camera, strain, and displacement sensors and then loaded until failure. The dominant damage mechanism was concrete cracking due to the over-reinforced design and adequate BFRP bar-concrete bonding. The AE method was evaluated in terms of identifying the damage initiation, progression from tensile to shear cracks, and the evolution of crack width. Unsupervised machine learning was applied to the AE data obtained from the first slab testing to develop the clusters of the damage mechanisms. The cluster results were validated using the k-means supervised learning model applied to the data obtained from the second slab. The accuracy of the K-NN model trained on the first slab was 99.2% in predicting three clusters (tensile crack, shear crack, and noise). Due to the limitation of a single indicator to characterize complex damage properties, a Statistical SHapley Additive exPlanation (SHAP) analysis was conducted to quantify the contribution of each AE feature to crack width. Based on the SHAP analysis, the AE duration had the highest correlation with the crack width. The cumulative duration of the AE sensor near the crack had close to 100% accuracy to track the crack width. It was concluded that the AE sensors positioned at the mid-span of slabs can be used as an effective SHM solution to monitor the initiation of tensile cracks, sudden changes in structural response due to major damage, damage evolution from tensile to shear cracks, and the progression of crack width.

Multi‐channel response reconstruction using transformer based generative adversarial network

AbstractAccurate measurement data are a basic prerequisite for effective structural health monitoring (SHM). However, data loss are inevitable in the long‐term monitoring of large‐scale structures. To solve this problem, this research proposes a transformer‐based generative adversarial network (GAN) to reconstruct lost measurements from observed measurements. The generator of GAN is an encoder‐decoder structure using transformer as the backbone combined with discrete wavelet transform. Skip connections are used between the encoder part and decoder part to promote multi‐scale information flow. A novel discriminator is designed to assess the reality of wavelet spectra of reconstructed samples. To deceive the discriminator, the generator must generate samples that are accurate over the full frequency band. The developed model is used to reconstruct linear responses of a footbridge under pedestrian excitations and nonlinear responses of a suspension bridge under typhoon events. Experimental results demonstrate that lost responses can be reconstructed accurately, even when a large proportion of data are lost. The effectiveness of the proposed method is further verified by comparing the reconstruction accuracy of the proposed model with those of other three state‐of‐the‐art models. The results demonstrate that an improved performance of applying the proposed approach for dynamic structural response reconstruction is achieved and validated with in‐field testing data under ambient and extreme excitation conditions.

Outlier Detection Based on Nelder-Mead Simplex Robust Kalman Filtering for Trustworthy Bridge Structural Health Monitoring

Structural health monitoring (SHM) is vital for ensuring the service safety of aging bridges. As one of the most advanced sensing techniques, Global Navigation Satellite Systems (GNSS) could capture massive spatiotemporal information for effective bridge structural health monitoring (BSHM). Unfortunately, GNSS measurements often contain outliers due to various factors (e.g., severe weather conditions, multipath effects, etc.). All such outliers could jeopardize the accuracy and reliability of BSHM significantly. Previous studies have examined the feasibility of integrating the conventional multi-rate Kalman filter (MKF) with an adaptive algorithm in the data processing processes to ensure BSHM accuracy. However, frequent parameter adjustments are still needed in tedious data processing processes. This study proposed an outlier detection method using a Nelder-Mead simplex robust multi-rate Kalman filter (RMKF) for supporting trustworthy BSHM using GNSS and accelerometer. In the end, the authors have validated the proposed method using the monitoring data collected at the Wilford Bridge in the UK. Results showed that the accuracy of the total dynamic vibration displacement time series has been improved by 21% compared with the results using the conventional MKF approach. The authors envision that the proposed method will shed light on reliable and explainable data processing policy and trustworthy BSHM.

An Electromechanical Impedance-Based Application of Realtime Monitoring for the Load-Induced Flexural Stress and Damage in Fiber-Reinforced Concrete

Effective real-time structural health monitoring in concrete structures is paramount to evaluating safety conditions and the timely maintenance of concrete structures. Especially, the presence of discrete fibers in fiber-reinforced concrete restrains crack propagation into small and thin cracks, which increases the difficulty in detecting damage. In this study, an array of piezoelectric lead zirconate titanate (PZT) transducers was applied to study the effects of external load-induced flexural stress and damage in fiber-reinforced concrete beams using the electromechanical impedance (EMI) or electromechanical admittance (EMA) methods. Beams were subjected to a four-point bending test under repeatable loading, while PZTs evaluated corresponding flexural stress and induced damage simultaneously. Due to the influence of the medium’s stress fields in the different types of wave propagation in structural elements, PZT transducers measurements are accordingly affected under variable stress fields, in addition to the effect of the higher level of damage that occurred in the medium. According to the results of the tests, variation in EMA signatures, following flexural stress and gradual damage changes, provided convincing evidence for predicting stress and damage development.

Two Dimensional Finite Difference Model with a Singularity Attenuation Factor for Structural Health Monitoring of Single Lap Joints

A finite difference algorithm that evaluates the health conditions of a bonded joint is presented and discussed. The mathematical formulation of the problem is developed, paying particular attention to the singularity around the corners of the joint and implementing an original discretisation method of the partial differential equations governing the propagation of the elastic waves. The equations are solved under the only hypothesis of a bidimensional field. The algorithm is sensible to defects in the bonded joint and can be used as an effective structural health monitoring tool, as proven by the experiments that show close agreement with the numerical simulations.

Detection of Nut–Bolt Loss in Steel Bridges Using Deep Learning Techniques

The early detection of bolts and nuts’ loss on bridges has a huge tendency of averting bridge collapse. The aim of this research is to develop a novel framework for the detection of bolt–nut losses in steel bridges using deep learning techniques. The objectives include: to design a framework for the detection of nuts and bolts and nut holes using deep learning techniques, to implement the designed framework using Python programming, and to evaluate the performance of the designed framework. Convolutional neural network (CNN) and long- and short-term memory (LSTM) techniques were employed using 8 × 8 blocks of images of bridges as inputs. Based on the proposed models, which considered the CNN in its ordinary form, and combined with the LSTM and You Only Look Once (YOLOv4) algorithms, the CNN achieved average classification accuracy of 95.60% and the LSTM achieved an accuracy of 93.00% on the sampled images. The YOLOv4 algorithm, which is a modified version of the CNN with single forward propagation, was utilized, and the detection accuracy was 76.5%. The relatively high level of detection accuracy recorded by the CNN is attributed to its stepwise extraction by convolution and pooling processes. However, a statistical test of the hypothesis at the 5.0% level of significance revealed that there was no statistically significant difference between object detection and classifications among the models used in the built framework. Therefore, the use of the CNN model is recommended for the detection of nuts and bolts and nut holes on steel truss bridges for effective structural health monitoring (SHM) purposes based on its high level of detection accuracy and speed.

Lamb waves-based technologies for structural health monitoring of composite structures for aircraft applications

The most common researched area of damage in a composite material such as carbon fibre reinforced plastics (CFRP) used currently in aircraft construction is barely visible impact damage (BVID), significantly reducing mechanical properties. Early detection and qualification would improve safety and reduce the cost of repair. In this context, structural health monitoring (SHM) techniques have been developed that could monitor a structure at any time by using a network of sensors. Widely used discrete ceramic transducers can generate and sense Lamb waves travelling in the structure. Wave propagation must then be analysed for effective damage identification. An effective SHM system is desired to meet several demands, such as minimised weight penalty, non-intrusive system not interfering with the structure performance, cost-effectiveness for implementation with targeted sensitivity and area coverage, capability of monitoring non-accessible and critical hot spot regions, robustness, and reliability. This review starts with an introduction on Lamb waves fundamentals and their use in SHM, and then particularly focuses on methods using piezoelectric transducers and mode selection. Some relevant applications on different structural configurations are discussed. Finally, recent developments on piezoelectric coating and direct-write sensor technology for tailored transducers are highlighted with some thoughts for near future research work.

Numerical investigation of the seismic vulnerability of bridge piers strengthened with steel fibre reinforced concrete (SFRC) and carbon fibre composites (CFC)

This research proposes a method aimed at investigating the detection of bridge damage areas using a rigorous method based on mathematical models of dynamic finite elements. Strengthening of the reinforced concrete (RC) is proposed for increasing the strength of RC structures when they present material degradation. It includes reinforcement with carbon reinforcement composites (CFC) and steel fiber reinforced concrete (SFRC), injection with SikaDur 52 Injection type epoxy resin, and the addition of SikaTop 126 type concrete. Establishment of a set of equations and numerical modeling of the specified method are presented in this study for sparse matrices of a large system of equations. A numerical solver with FEM Ansys software was used to avoid the lack of big-data memory storage on the available hardware. RC material properties have been modeled with the composite material mixture laws as a homogeneous material. Several simulations have been achieved. The results obtained allowed location of stress concentration areas and consequently, detection of important von-Mises equivalent stresses of strength bound in severe load cases as well as graphically visualization of the time history of displacements evolution. The study has also revealed that the reliability design of the probabilistic method with the security margin as a stochastic variable is an effective structural health monitoring approach.

Signal Processing Methodology of Response Data from a Historical Arch Bridge toward Reliable Modal Identification

The paper is part of a case study concerning the structural assessment of a historical infrastructure in the local territory, a road three-span reinforced concrete arch bridge over a river, built by the end of World War I (1917). The purpose of the paper is twofold: first, in-situ acquired response data are systematically analysed by specific signal processing techniques, to form a devoted methodological procedure and to extract useful information toward possible interpretation of the current structural conditions; second, the deciphered information is elaborated, in view of obtaining peculiar conceptualisations of detailed features of the structural response, as meant to achieve quantitative descriptions and modelling, for final Structural Health Monitoring (SHM) and intervention purposes. The proposed methodology, integrating self-implemented and adapted classical signal processing methods, and refined techniques, such as Wavelet analysis and ARMA models, assembles a rather general, systematic methodological approach to signal processing, highlighting the capability to extract useful and fundamental information from acquired response data, also endowed of a non-stationary character, toward final structural interpretation, identification and modelling, thus enabling for developing a reliable and effective SHM platform, on strategic ageing infrastructures. For the present case study, non-stationary characteristics of the response signals are revealed and flattened out, to identify the underlying fundamental frequencies of the infrastructure and to advance particular interpretations of its current structural behaviour, in forming an enlarging structural consciousness of the bridge at hand.

Damage identification of wind turbine blades using the microphone array under different parametric and measuring conditions: A prototype study with laboratory-scale models

Structural health monitoring (SHM) of wind turbine blades is significant to the reliability and efficiency of wind energy generation, and it is a challenging issue due to the complicated structures and variational operating conditions. In this investigation, a SHM method for wind turbine blades based on the microphone array and acoustic source identification is proposed. With the equipment of loudspeakers in blade cavities, damage-related information is excited to be captured by the array. To generate accurate acoustic maps with high spatial resolutions, a novel algorithm for sparsity-based sound field reconstruction is developed based on the generalized minimax-concave penalty function. With a laboratory-scale wind turbine model, damage identification performance of the proposed method is evaluated under different parametric and measuring conditions, and experiments are conducted under diverse blade health conditions. Results reveal that and both internal and external damage in operating blades can be recognized as acoustic sources, and satisfactory performance of the proposed method can be guaranteed with appropriate parameters. Furthermore, determination criteria for parameters are concluded with respect to the variation of measuring conditions. This prototype study provides useful insights into the development of effective SHM systems.