Blades play a critical role in the wind turbine system. Therefore, their structural health monitoring is very important. Blades are damaged by sudden changes in wind load, cracks due to collision of foreign objects, and disasters, such as lightning strikes, hail, and typhoons. Moreover, blades are expensive to maintain. Defects or damages to wind turbine blades reduce the life span and power generation

To gain an insight into the evolution of micro-cracks in concrete materials, a quantitative acoustic emission investigation on the damage process of concrete prisms subjected to three-point bending loading was performed. Each of the monitored acoustic emission signals was processed by a two-level wavelet packet decomposition into four different frequency bands (AA2, DA2, AD2, and DD2), and the energy

Structural health monitoring of impact location and severity using Lamb waves has been proven to be a reliable method under laboratory conditions. However, real-life operational and environmental conditions (vibration noise, temperature changes, different impact scenarios, etc.) and measurement errors are known to generate variation in Lamb wave features which may significantly affect the accuracy

Transient impulses caused by local faults are critical informative indicators for rolling element bearing fault diagnosis. The methods for accurately extracting transient impulses while suppressing strong background noise and interference components have received extensive studies. In this article, a novel fault diagnosis scheme based on optimized wavelet packet denoising and modulation signal bispectrum

The detection and localization of structural damage in a stiffened skin-to-stringer composite panel typical of modern aircraft construction can be addressed by ultrasonic-guided wave transducer arrays. However, the geometrical and material complexities of this part make it quite difficult to utilize physics-based concepts of wave scattering. A data-driven deep learning (DL) approach based on the convolutional

To perform long-term structural health monitoring, a method based on a nonlinear autoregressive exogenous network is used to learn the features present in signals acquired from a pristine structure. When a subsequent measured signal is input to the trained nonlinear autoregressive exogenous network, the output is a prediction of the equivalent signal from a pristine structure. The residual when the

As extreme events increase in frequency, flow-disrupting large-scale structures become ever more susceptible to collapse due to local scour effects. The objective of this study was to validate the functionality of passive, flow-excited scour sensors that can continue to operate during an extreme event. The scour sensors, or piezo-rods, feature continuous piezoelectric polymer strips embedded within

Irrespective of the popularity and demonstrated effectiveness of ultrasound tomography (UT) for damage evaluation, reconstruction of a precise tomographic image can only be guaranteed when a dense transducer network is used. However, a network using transducers such as piezoelectric wafers integrated with the structure under inspection unavoidably lowers local material strength and consequently degrades

The l1 regularization technique has been developed for damage detection by utilizing the sparsity feature of structural damage. However, the sensitivity matrix in the damage identification exhibits a strong correlation structure, which does not suffice the independency criteria of the l1 regularization technique. This study employs the elastic net method to solve the problem by combining the l1 and

Wireless sensors are the key components of structural health monitoring systems. During the signal transmission, sensor failure is inevitable, among which, data loss is the most common type. Missing data problem poses a huge challenge to the consequent damage detection and condition assessment, and therefore, great importance should be attached. Conventional missing data imputation basically adopts

Displacement is the most intuitive reflection of the structural behaviour of concrete dams, and it is of great significance to predict future displacement for dam health diagnosis. The mathematical models used to predict displacement are mostly established with the only regression objective of minimizing the mean square error between the measured and fitted displacements, whereas the spatial associations

Delamination in composite laminates reduces the structural stiffness and thus causes changes in the vibration responses of the laminates. Therefore, it is feasible to employ dynamic characteristics (such as natural frequencies and mode shapes) for delamination detection by using an optimization method. In the present study, a two-step method is proposed for the delamination detection in composite laminates

Signal processing is one of the essential components in vibration-based approaches and damage detection for structural health monitoring. Since signals in the real world are often nonlinear and non-stationary, especially in extended and complex structures, such as bridges, the Hilbert–Huang transform is used for damage assessment. In recent years, the empirical mode decomposition technique has been

Vibration-based condition identification of bolted connections can benefit the effective maintenance and operation of steel structures. Existing studies show that modal parameters are not sensitive to such damage as loss of preload. In contrast, structural responses in the time domain contain all the information regarding a structural system. Therefore, this study aims to exploit time-domain data directly

Deep learning algorithm can effectively obtain damage information using labeled samples, and has become a promising feature extraction tool for ultrasonic guided wave detection. But it is difficult to apply the monitoring expertise of structure A to structure B in most cases due to the differences in the dispersion and receiving modes of different waveguides. For multi-structure monitoring at the system

The unavoidable increase in train speed and load, as well as the aging of railway facilities, is requiring more and more attention to rail defects detection. As a promising tool for rail, in-service high-speed inspection, guided wave–based detection technologies have been developed in succession by researches in the past two decades. However, there is a lack of a systematic review on the developments

Worldwide, asset managers are struggling with the management of ageing infrastructure in reinforced concrete. Early detection of reinforcement corrosion, which is generally considered as the major problem, can help to perform dedicated maintenance and repair. The acoustic emission technique is promising to reach this goal. However, research on the characterisation of the different damage sources during

The core of structural health monitoring is to provide a real-time monitoring, inspection, and damage detection of structures. As one of the most promising technology to structural health monitoring, the Lamb wave method has attracted interest because it is sensitive to small-scale damage with a long detection range. However, in many real-world structural health monitoring applications, the nature

This article presents a unique method of installing a special type of embedded ultrasonic transducers inside a 36-m-long section of an old bridge in Germany. A small-scale load test was carried out by a 16 ton truck to study the temperature and load influence on the bridge, as well as the performance of the embedded transducers. Ultrasonic coda wave interferometry technique, which has high sensitivity

Bridge inspection is an important step in preserving and rehabilitating transportation infrastructure for extending their service lives. The advancement of mobile robotic technology allows the rapid collection of a large amount of inspection video data. However, the data are mainly the images of complex scenes, wherein a bridge of various structural elements mix with a cluttered background. Assisting

Pipeline block and pipeline leak may lead to serious accidents and cause huge economic losses, which have been urgent problems for gas transportation. In this work, active acoustic pulse-compression technology is first introduced to detect and locate these two anomalous events. The matched filtered signals are then normalized and input into one-dimensional convolutional neural network to achieve classification

The estimation of the damping coefficient may help to improve the damage detection in composite materials. The purpose of this study was to develop the simulated Lamb wave propagation method for nondestructive monitoring of matrix cracking in laminated composites via the accurate estimation of their damping coefficient. Cross-ply composite specimens with different crack densities were fabricated and

A critical problem encountered in structural health monitoring of civil engineering structures, and other structures such as mechanical or aircraft structures, is how to convincingly analyze the nonstationary data that is coming online, how to reduce the high-dimensional features, and how to extract informative features associated with damage to infer structural conditions. Wavelet transform among

In general, the bolted joints that connect and secure components together can be easily spotted in our surroundings. This joining method has been commonly used in various areas of engineering (e.g. aerospace, civil, and mechanical engineering) as it has been proven one of the most effective means to join parts together. Although it has its advantages, the vibration that bolted structures endure during

The interaction of guided waves with a material discontinuity is not well understood. This study investigates the propagation behavior of the fundamental Lamb-wave modes, the symmetric mode (S0) and the anti-symmetric mode (A0), upon interaction with welded joints of dissimilar materials. A plate with an intact AA6061-T6/AZ31B dissimilar joint was employed, and the interaction of the propagating wave

Long-span suspension bridges are susceptible to wind loads due to their lightweight, low stiffness, and small structural damping. Recently, two large-span suspension bridges in China that closed for several months due to COVID-2019 experienced large-scale and continuous vortex-induced vibration shortly after reopening to traffic, and the traffic was closed again for safety consideration, which has

This study aims to investigate the performance of a data-driven methodology for quantifying damage based on the use of a metamodel obtained from the Polynomial Chaos-Kriging method. The investigation seeks to quantify the severity of the damage, described by a specific type of debonding in a wind turbine blade as a function of a damage index. The damage indexes used are computed using a data-driven

Crack assessment of reinforced concrete structures using stereo cameras is a potential way for increasing the efficiency and safety of infrastructure maintenance routines. However, existing damage methods for reinforced concrete structures are based on the segmentation of two-dimensional planes without consideration to the actual size of concrete damage. Furthermore, on-site structural monitoring requires

With the rapid development of information and communication techniques, dam structural health assessment based on data collected from structural health monitoring systems has become a trend. This allows for applying data-driven methods for dam safety analysis. However, data-driven models in most related literature are statistical and shallow machine learning models, which cannot capture the time series

Conducting damage diagnostics on stiffened panels is commonly performed using a single SHM technique. However, each SHM technique has both its strengths and limitations. Rather than straining the expansion of single SHM techniques going beyond their intrinsic capacities, these strengths and limitations should instead be considered in their application. In this work, we propose a novel fusion-based

Corrosion is considered a destructive phenomenon that affects almost all metals. Active infrared thermography is an online (no result delay) and non-intrusive (no process disruption) method of non-destructive testing (NDT), which has shown profound capabilities of detecting sub-surface metal loss. However, thermal reflections, non-uniform stimulation and lateral heat diffusion will remain as the most

Dealing with the problem of large volumes of high-dimensional features and detecting damage under ambient vibration are critical to structural health monitoring. To address these challenges, this article proposes a novel data-driven method for early damage detection of civil engineering structures by robust multidimensional scaling. The proposed method consists of some simple but effective computational

The metallic panel acoustic emission signal with strong non-stationary properties is normally composed of multiple components (e.g. impulses, background noise, and other external signal), where impulses relevant to metallic panel are easily contaminated by background noise and other external signal, making it difficult to excavate the inherent acoustic emission signal features. To address this issue

Different from traditional health-monitoring methods based on vibrational signals recorded by contact sensors, an online diagnosis procedure for steel truss structures using sound signals was proposed. The basic idea of the procedure was to identify the features related to bolt connection damage extracted from sound signals and locate the damaged position. Before the online diagnosis was carried out

Achieving a high prediction rate is a crucial task in fault detection. Although various classification procedures are available, none of them can give high accuracy in all applications. Therefore, in this article, a novel multi-classifier fusion approach is developed to boost the performance of the individual classifiers. This is acquired by using Dempster–Shafer theory. However, in cases with conflicting

Ice-induced impact loads on the propeller blades of vessels operating in polar waters represent a notable hazard to ship propulsion systems. Industry guidelines determine the maximum allowable ice-induced propeller moments, but these loads are not practical to measure directly. Recent studies have implemented deterministic inversion methods to determine these impact-induced moments from torque measurements

A novel, unsupervised, hypersphere-based healthy subspace method for robust damage detection under non-quantifiable uncertainty via a limited number of random vibration response sensors is postulated. The method is based on the approximate construction, within a proper feature space, of a healthy subspace representing the healthy structural dynamics under uncertainty as the union of properly selected

To promote the development of structural health monitoring around the world, the 1st International Project Competition for Structural Health Monitoring (IPC-SHM, 2020) was initiated and organized in 2020 by the Asia-Pacific Network of Centers for Research in Smart Structures Technology, Harbin Institute of Technology, the University of Illinois at Urbana-Champaign, and four leading companies in the

The fault feature signal of rolling bearing can be characterized as the narrow-band signal with a specific resonance frequency. Therefore, resonance demodulation analysis is a powerful damage detection technique of bearings. In addition to the fault feature signal, the measured vibration signals carry various interference components, and these interference components become a serious obstacle of fault

The bridges monitored within one cluster refer to several medium- and small-span beam bridges with the same or similar structural characteristics located in a local road network, and these bridges suffer the similar traffic and environmental loads. It is still little research on how to detect the damage of all bridges monitored within one cluster utilizing the above-mentioned characteristics of these

The seven-wire strands are the crucial components of prestressed structures, though their performance inevitably degrades with the passage of time. The ultrasonic guided wave methods have been intensely studied, owing to its tremendous potential for full-scale applications, among the existing nondestructive testing methods, for evaluating the stress status of strands. We have employed the theoretical

Wind energy is considered as one of the most important renewable energies in the world, employing larger and more complex wind turbines. They need novel condition monitoring systems to ensure the reliability, availability, safety and maintainability of the main components of the wind turbines. It leads to early fault detection, increasing the productivity and minimizing the maintenance costs and downtimes

The use of optic measurements such as digital image correlation to take strain measurements of fibre-reinforced polymers bonded to a substrate has been on the increase recently. This technique has proven to be useful to fully characterize the bond behaviour between two materials. Although modern digital cameras can take high-definition photos, this task is far from simple due to the tiny displacements

This study proposes a probabilistic framework for near real-time seismic damage assessment that exploits heterogeneous sources of information about the seismic input and the structural response to the earthquake. A Bayesian network is built to describe the relationship between the various random variables that play a role in the seismic damage assessment, ranging from those describing the seismic source

A non-destructive testing procedure is proposed for damage detection in composites using full wavefield measurement obtained using a scanning laser Doppler vibrometer. Vibrations are excited using two low-power piezoelectric actuators leading to nonlinear elastic wave modulation at the defect. One actuator is supplied with a broadband chirp signal and the other actuator is supplied with a single-frequency

This study proposes a new sensor cluster configuration for localizing an acoustic source in a plate using uniform linear array beamforming and T-shaped sensor clusters. This technique requires neither the properties of the plate material nor a dense array of sensors to find the direction of arrival of the acoustic source. It functions by placing four sensors in a cluster in the shape of the letter

With the development of deep learning, object detection algorithms based on horizontal box are widely used in the field of damage identification. However, damages can be in any direction and position, and they are not necessarily horizontal or vertical. This article proposes a bolt damage identification network, namely, orientation-aware center point estimation network, which models a damage as a center

This study presents a three-dimensional global–local formulation for the prediction of guided wave scattering from discontinuities (e.g. defects). The approach chosen utilizes the Semi-Analytical Finite Element method for the “global” portion of the waveguide, and a full Finite Element discretization for the “local” portion of the waveguide containing the discontinuity. The application of interest

This study is devoted to the use of acoustic emission technique for a comprehensive damage assessment, that is, damage detection, localization, and classification, of an aeronautical metal-to-composite bonded panel. The structure comprised a titanium panel adhesively bonded to carbon fiber–reinforced plastic omega stringers. The panel contained a small initial artificial debonding between the titanium

Damage mode identification and premature failure prevention for composite structures by acoustic emission have drawn a great deal of attention. Feature evaluation on streaming acoustic emission data is one of the significant issues in research of acoustic emission signal processing. This work conducts dynamic feature evaluation on 15 conventional acoustic emission features so as to seek a deeper insight

Damage detection methods developed in the literature are affected by the presence of outlier measurements. These measurements can prevent small levels of damage to be detected. Therefore, a method to eliminate the effects of outlier measurements is proposed in this article. The method uses the difference in fits to examine how deleting an observation affects the predicted value of a model. This allows

A robust real-time damage detection technique of earthquake-excited structures based on a new demodulation technique for nonlinear and non-stationary vibration signals through the identification of signal envelopes in real time is presented. In the present work, the need for the detection of envelope in a vibration signal in real time is addressed by reformulating the concept of Hermitian interpolation

The vast majority of the existing diagnostic studies using deep learning techniques for rotating machinery focus on the vibration analysis under steady rotating speed. Nevertheless, the collected vibration signals are sensitive to time-varying speeds and the vibration sensors may cause structure damage of equipment after long-term close contact. Aiming at these aforementioned problems, a modified Gaussian

Signal instability due to temperature fluctuations, sensor degradation, and debonding introduces additional amplitude loss in the detected signals during acousto-ultrasonic detection, which may be falsely attributed to defects in a structure. First, we determined that the amplitudes of both high-frequency and low-frequency Lamb waves decrease after propagation through a damaged area. Then, we found

The article presents a literature review of electromechanical impedance spectroscopy for structural health monitoring, with emphasis in adhesively bonded joints. The concept behind electromechanical impedance spectroscopy is to use variable high-frequency structural vibrations with piezoelectric elements to monitor the local area of a structure for changes in mechanical impedance that may indicate

Structural health monitoring system plays a vital role in smart management of civil engineering. A lot of efforts have been motivated to improve data quality through mean, median values, or simple interpolation methods, which are low-precision and not fully reflected field conditions due to the neglect of strong spatio-temporal correlations borne by monitoring datasets and the thoughtless for various

Concrete dam health state has been taken seriously in the field of structural health monitoring. A limitation in the current research is a lack of clear physical significance and reasonable interval division for better reflecting the development trend of the health state. This article introduces a comprehensive evaluation method that provides information on the fusion of different monitoring types

This study proposes a combined machine learning–wave propagation approach for nondestructive prediction of the modulus of elasticity (MOE) and rupture (MOR) of timber subjected to ultraviolet (UV) radiation. Fir, poplar, alder, and oak wood specimens were subjected to artificial UV aging and assessed using the Lamb wave propagation. Different features including the wave characteristics and the viscoelastic

Life-cycle cost analysis is an approach that has gained popularity for assisting the design of civil infrastructures. The life-cycle cost analysis approach can be leveraged for structures equipped with structural health monitoring systems in order to quantify the benefits of the technology and de facto support its long-term implementation. However, for new structures, the long-term assessment of the

Identification of nonlinear restoring force and dynamic loadings provides critical information for post-event damage diagnosis of structures. Due to high complexity and individuality of structural nonlinearities, it is difficult to provide an exact parametric mathematical model in advance to describe the nonlinear behavior of a structural member or a substructure under strong dynamic loadings in practice