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The current image processing techniques heavily rely on classic windows for suppressing spectral leakage. However, the limited capability of these classic windows to suppress spectral leakage restricts their application in the field of high-precision measurement. This paper proposes a novel family of windows capable of suppressing spectral leakage associated with the Moiré fringes pattern, thereby enhancing the accuracy of lithography alignment. Specifically, a novel two-dimensional Hanning self-convolution window is introduced, which is derived from the Hanning window to inherit its advantage of ultra-rapid sidelobe decay. The behaviors of the main lobe and desirable sidelobes of the 1st- to 4th-order are examined. Additionally, a novel fringe phase extraction algorithm is established based on the proposed window, which demonstrates high accuracy and efficiency. Simulation and experimental results validate the feasibility and practicality of the proposed window. In particular, the proposed window outperforms classic windows in suppressing spectral leakage, thereby successfully enhancing the phase extraction accuracy to achieve high-precision misalignment measurement at the sub-2-nm level (1.47 nm @ 3σ criterion). The proposed method can be easily extended to other real engineering applications utilizing fringe to achieve high-precision measurements, such as fringe projection profilometry, damage detection, and visual mechanical vibration monitoring. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing interest in the development of ground-based Synthetic Aperture Radars (GBSAR) for the purpose of monitoring structural displacements. GBSAR offers high-resolution monitoring over a wide area and can capture data every few minutes. However, compact high-frequency multiple input multiple output (MIMO) radars have emerged as an alternative for monitoring sub-second displacements, such as structural vibrations. MIMO radar has sub-second acquisition interval. However, it has limited cross-range resolution compared to GBSAR, and interference between antennas and presence of multiple scatterers in the scene can cause strong sidelobes in the processed data. On the other hand, GBSAR utilizes a long synthetic aperture to achieve high cross-range resolution. However, due to its longer data acquisition time compared to MIMO radar, conventional methods are insufficient for detecting scatterers' sub-second displacements that occur during the data acquisition process. This study proposes a method to effectively monitor sub-second or sub-minute displacements using GBSAR signals. The proposed method enhances the conventional radar interferometric processes by employing spectral estimation, allowing for multi-dimensional detection of targets' azimuth angle, linear displacement, and vibrational characteristics. Consequently, this method improves both the processing of MIMO radar data and enables high-resolution fast displacement monitoring from GBSAR signals. The paper presents the theoretical details and mathematical formulations of the proposed method for both MIMO radar and GBSAR imaging modes. To evaluate the effectiveness of the proposed method, numerical simulations and real experiments are conducted. The experimental results validate the capability of the proposed method in both GBSAR and MIMO configuration modes for high-resolution monitoring of fast linear displacements and vibrations. The results exhibit promising signal-to-noise ratio (SNR) and peak-to-sidelobe ratio (PSLR) values. [ABSTRACT FROM AUTHOR]

External threats including excavation, drilling, construction, vandalism, or sabotage can result in pipeline leak or rupture. To improve pipeline safety, an AI-based monitoring system for buried pipeline external disturbance detection and classification is proposed. The system is designed to detect and recognize potential threats prior to the occurrence of damage, offering localized continuous non-invasive structural health monitoring. In the proposed system, an accelerometer measures the operational vibration of the buried pipeline due to excitation by both internal flow turbulence and external disturbances. In this paper, the characteristics of both internal and external disturbances are investigated including pressure perturbations, drilling, jumping, running, and walking. The designed monitoring system consists of two processing phases. In the first phase, a disturbance indicator is developed using common time–frequency analysis techniques to generate a Spectral Index Function (SIF) and a Severity Index (SI), and these are used as inputs to a Quadratic Support Vector Machine (Q-SVM) classifier. Four classifications are used and overall accuracies of above 99% are achieved. In the second phase, contributions due to normal operating conditions are removed using a wavelet denoising technique. Morphological features of external disturbances are represented by grayscale images created by performing Hilbert Spectral Analysis (HSA) on wavelet denoised time series. A four-layer Convolutional Neural Network (CNN) classifier with inception modules is designed to perform the classification task. The results show that external disturbances can be successfully classified with high accuracy (96.1%). The robustness of the monitoring system is examined using data sets collected from different accelerometer locations. The results show that the designed monitoring system has a high level of robustness. [ABSTRACT FROM AUTHOR]

Abstract Vibration induced fatigue is a well-known problem in oil and gas piping systems. However the use of vibration data to detect damage is not an easy task without a priori knowledge of the undamaged condition. In this paper, level 1: Detection and level 2: Localization of damage from structural health monitoring strategies is adapted for damage identification. An experimental trial of acoustic emission monitoring was run to monitor fatigue damage during a full-scale resonance fatigue test of a girth-welded steel pipe from healthy condition until failure. The pipe was excited into the first mode of vibration using a resonance fatigue testing machine in order to determine the high-cycle fatigue strength of the weld. The information provided by acoustic emission monitoring is useful in evaluating the condition of the pipe during the test and the occurrence of cracking before failure. However, the acoustic emission signals are embedded in noise. To overcome this problem, the signals from different combinations of sensors were recursively cross-correlated, which provides for the derivation of a new effective coefficient (EC) parameter for Bayesian estimation. This parameter is useful for evaluating uncertainty arising from the signals that contribute to source localization errors. The estimation finds the most probable parameters corresponding to cracks using prior knowledge derived from standard pencil lead break tests. The proposed method demonstrates a strong relationship between the acoustic emission energy and the estimated coefficients. A high correlation between signals was found to be associated with cracking, and a low correlation between signals was found to be associated with random signals or noise. The method will be useful for monitoring the condition of piping to manage the risk of vibration induced fatigue failure. [ABSTRACT FROM AUTHOR]

The application of energy harvesting technology for monitoring civil infrastructure is a bourgeoning topic of interest. The ability of kinetic energy harvesters to scavenge ambient vibration energy can be useful for large civil infrastructure under operational conditions, particularly for bridge structures. The experimental integration of such harvesters with full scale structures and the subsequent use of the harvested energy directly for the purposes of structural health monitoring shows promise. This paper presents the first experimental deployment of piezoelectric vibration energy harvesting devices for monitoring a full-scale bridge undergoing forced dynamic vibrations under operational conditions using energy harvesting signatures against time. The calibration of the harvesters is presented, along with details of the host bridge structure and the dynamic assessment procedures. The measured responses of the harvesters from the tests are presented and the use the harvesters for the purposes of structural health monitoring (SHM) is investigated using empirical mode decomposition analysis, following a bespoke data cleaning approach. Finally, the use of sequential Karhunen Loeve transforms to detect train passages during the dynamic assessment is presented. This study is expected to further develop interest in energy-harvesting based monitoring of large infrastructure for both research and commercial purposes. [ABSTRACT FROM AUTHOR]

Structural Health Monitoring (SHM) is the engineering discipline of diagnosing damage and estimating safe remaining life for structures and systems. Often, SHM is accomplished by detecting changes in measured quantities from the structure of interest; if there are no competing explanations for the changes, one infers that they are the result of damage. If the structure of interest is subject to changes in its environmental or operational conditions, one must understand the effects of these changes in order that one does not falsely claim that damage has occurred when changes in measured quantities are observed. This problem – the problem of confounding influences – is particularly pressing for civil infrastructure where the given structure is usually openly exposed to the weather and may be subject to strongly varying operational conditions. One approach to understanding confounding influences is to construct a data-based response surface model that can represent measurement variations as a function of environmental and operational variables. The models can then be used to remove environmental and operational variations so that change detection algorithms signal the occurrence of damage alone. The current paper is concerned with such response surface models in the case of SHM of bridges. In particular, classes of response surface models that can switch discontinuously between regimes are discussed. Recently, it has been shown that Gaussian Process (GP) models are an effective means of developing response surface or surrogate models. However, the GP approach runs into difficulties if changes in the latent variables cause the structure of interest to abruptly switch between regimes. A good example here, which is well known in the SHM literature, is given by the Z24 Bridge in Switzerland which completely changed its dynamical behaviour when it cooled below zero degrees Celsius as the asphalt of the deck stiffened. The solution proposed here is to adopt the recently-proposed Treed Gaussian Process (TGP) model as an alternative. The approach is illustrated here on the Z24 bridge and also on data from the Tamar Bridge in the UK which shows marked switching behaviour in certain of its dynamical characteristics when its ambient wind conditions change. It is shown that treed GPs provide an effective approach to response surface modelling and that in the Tamar case, a linear model is in fact sufficient to solve the problem. [ABSTRACT FROM AUTHOR]

The subject of structural health monitoring is drawing more and more attention over the last years. Many vibration-based techniques aiming at detecting small structural changes or even damage have been developed or enhanced through successive researches. Lately, several studies have focused on the use of raw dynamic data to assess information about structural condition. Despite this trend and much skepticism, many methods still rely on the use of modal parameters as fundamental data for damage detection. Therefore, it is of utmost importance that modal identification procedures are performed with a sufficient level of precision and automation. To fulfill these requirements, this paper presents a novel automated time-domain methodology to identify modal parameters based on a two-step clustering analysis. The first step consists in clustering modes estimates from parametric models of different orders, usually presented in stabilization diagrams. In an automated manner, the first clustering analysis indicates which estimates correspond to physical modes. To circumvent the detection of spurious modes or the loss of physical ones, a second clustering step is then performed. The second step consists in the data mining of information gathered from the first step. To attest the robustness and efficiency of the proposed methodology, numerically generated signals as well as experimental data obtained from a simply supported beam tested in laboratory and from a railway bridge are utilized. The results appeared to be more robust and accurate comparing to those obtained from methods based on one-step clustering analysis. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a new damage detection methodology for beams. It applies wavelet analysis to locate the damage from changes in the mode shapes (geometric based analysis). The proposed methodology requires the mode shapes of a reference undamaged state as well as those of the potentially damaged one. Once obtained, a continuous wavelet transform is applied to the difference of the mode shape vectors to obtain information of the changes in each of them. Finally, the results for each mode are added up to compute an overall result along the structure. For the addition, the wavelet coefficients of each mode are weighted according to the corresponding variation of the natural frequency. By doing so, emphasis is given on those modes that are more likely to be affected by damage. On the other hand, mode shapes that have not changed their natural frequencies are disregarded. The proposed methodology also includes mathematical techniques to avoid wavelet transform edge effect, experimental noise reduction in mode shapes and creation of new virtual measuring points. It has been validated by experimental analysis of steel beams with cracks of different sizes and at different locations. The results show that the method is sensitive to little damage. The paper analyses the severity threshold of damage and the required number of sensors to obtain successful results. [Copyright &y& Elsevier]

The study focuses on vibration response based health monitoring for an operating wind turbine, which features time-dependent dynamics under environmental and operational uncertainty. A Gaussian Mixture Model Random Coefficient (GMM–RC) model based Structural Health Monitoring framework postulated in a companion paper is adopted and assessed. The assessment is based on vibration response signals obtained from a simulated offshore 5 MW wind turbine. The non–stationarity in the vibration signals originates from the continually evolving, due to blade rotation, inertial properties, as well as the wind characteristics, while uncertainty is introduced by random variations of the wind speed within the range of 10–20 m/s. Monte Carlo simulations are performed using six distinct structural states, including the healthy state and five types of damage/fault in the tower, the blades, and the transmission, with each one of them characterized by four distinct levels. Random vibration response modeling and damage diagnosis are illustrated, along with pertinent comparisons with state–of–the–art diagnosis methods. The results demonstrate consistently good performance of the GMM–RC model based framework, offering significant performance improvements over state–of–the–art methods. Most damage types and levels are shown to be properly diagnosed using a single vibration sensor. [ABSTRACT FROM AUTHOR]

Continuously identified natural frequencies of vibration can provide unique information for low-cost automated condition assessment of civil constructions and infrastructures. However, the effects of changes in environmental parameters, such as temperature and humidity, need to be effectively investigated and accurately removed from identified frequency data for an effective performance assessment. This task is particularly challenging in the case of historical constructions that are typically massive and heterogeneous masonry structures characterized by complex variations of materials' properties with varying environmental parameters and by a differential heat conduction process where thermal capacity plays a major role. While there is abundance of documented monitoring data highlighting correlations between environmental parameters and natural frequencies in the case of new structures, such as long-span bridges, similar studies for historical constructions are still missing, with only a few literature works occasionally reporting increments in natural frequencies with increasing temperature of construction materials due to the closure of internal micro-cracks in the mortar layers caused by thermal expansion. In order to gain some knowledge on the effects of changes in temperature and humidity on the natural frequencies of slender masonry buildings, the paper focuses on the case study of an Italian monumental bell tower that has been monitored by the authors for more than nine months. Correlations between natural frequencies and environmental parameters are investigated in detail and the predictive capabilities of linear statistical regressive models based on the use of several environmental continuous monitoring sensors are assessed. At the end, three basic mechanisms governing environmentally-induced changes in the dynamic behavior of the tower are identified and essential information is achieved on the optimal location and minimum number of environmental sensors that are necessary in a structural health monitoring perspective. [ABSTRACT FROM AUTHOR]

Structural health monitoring of wind turbines is usually performed by collecting real-time operating data on a limited number of accessible locations using traditional sensors such as accelerometers and strain-gauges. When dealing with offshore wind turbines (OWT) though, most of the fatigue sensitive spots are inaccessible for direct measurements, e.g. at the mudline below the water level. Response estimation techniques can then be used to estimate the response at unmeasured locations from a limited set of response measurements and a Finite Element Model. In this paper the method will be validated using accelerations only. The method makes use of a modal decomposition and expansion algorithm that allows for successful response prediction. The algorithm is first validated using simulated datasets provided from HAWC2 and then using real time data obtained from a monitoring campaign on an offshore Vestas V90 3 MW wind turbine on a monopile foundation in the Belgian North Sea. [ABSTRACT FROM AUTHOR]

The study described in this paper revisits the concept of instantaneous identification of system parameters based on time–frequency representations. In order to overcome the distortion caused by the time–frequency analysis window, optimal bias-compensated estimators are introduced. In particular, bias-compensated estimators are conceived specifically to provide on-line estimates for time-varying linear parameters such as instantaneous frequency and damping. With respect to previous studies, which relied on the concept of optimal time–frequency representation, the novel procedure corrects on-line estimates provided by standard representations. Afterwards, a practical application to a full-scale structure is presented. The church of “Madonnina della Neve” in Savigliano (Cuneo province, Italy) is a masonry building that exhibits defects in the connections between structural parts and visible cracks in lateral masonry walls. As a consequence, the global behaviour observed on this structure demonstrates significant flexibility in both the longitudinal and transversal directions. Recently cracks have worsened due to vibrations induced by traffic and heavy vehicles. Proposed bias-compensated estimators were used to analyse the unusual time-varying behaviour of the masonry structure, as observed from the response measured during the transit of vehicles and trucks. [ABSTRACT FROM AUTHOR]

Abstract: The motivation of the paper is to develop a fully automated data-based technique for damage localization using in-service ambient vibrations. The idea is an extension of the modal filtering technique previously developed for damage detection. A very large network of dynamic strain sensors is deployed on the structure to be monitored and split into several independent local sensor networks. Simple and fast signal processing techniques are coupled to statistical control charts for efficient and fully automated damage localization. The efficiency of the method is demonstrated using time-domain simulated data on a simply supported beam and a three-dimensional bridge structure. The method is able to detect and locate very small damages (2% stiffness reduction in an area corresponding to 1/100th of the length of the structure) even in the presence of noise on the measurements and variability of the baseline structure. [Copyright &y& Elsevier]

Abstract: Power harvesting techniques that convert vibration energy into electrical energy through piezoelectric transducers show strong potential for powering smart wireless sensor devices in applications of structural health monitoring. This paper presents an analytical model of the dynamic behavior of an electromechanical piezoelectric bimorph cantilever harvester connected with an AC–DC circuit based on the Euler–Bernoulli beam theory and Hamiltonian theorem. A new cantilevered piezoelectric bimorph structure is proposed in which the plug-type connection between support layer and tip-mass ensures that the gravity center of the tip-mass is collinear with the gravity center of the beam so that the brittle fracture of piezoelectric layers can also be avoided while vibrating with large amplitude. The tip-mass is equated by the inertial force and inertial moment acting at the end of the piezoelectric bimorph beam based on D''Alembert''s principle. An AC–DC converting circuit soldered with the piezoelectric elements is also taken into account. A completely new analytic expression of the global behavior of the electromechanical piezoelectric bimorph harvesting system with AC–DC circuit under input base transverse excitation is derived. Moreover, an experimental energy harvester is fabricated and the theoretical analysis and experimental results of the piezoelectric harvester under the input base transverse displacement excitation are validated by using measurements of the absolute tip displacement, electric voltage response, electric current response and electric power harvesting. [Copyright &y& Elsevier]

Abstract: In order to evaluate the usefulness of approaches based on modal parameters tracking for structural health monitoring of bridges, in September of 2007, a dynamic monitoring system was installed in a concrete arch bridge at the city of Porto, in Portugal. The implementation of algorithms to perform the continuous on-line identification of modal parameters based on structural responses to ambient excitation (automated Operational Modal Analysis) has permitted to create a very complete database with the time evolution of the bridge modal characteristics during more than 2 years. This paper describes the strategy that was followed to minimize the effects of environmental and operational factors on the bridge natural frequencies, enabling, in a subsequent stage, the identification of structural anomalies. Alternative static and dynamic regression models are tested and complemented by a Principal Components Analysis. Afterwards, the identification of damages is tried with control charts. At the end, it is demonstrated that the adopted processing methodology permits the detection of realistic damage scenarios, associated with frequency shifts around 0.2%, which were simulated with a numerical model. [Copyright &y& Elsevier]

Abstract: This paper presents a study for identifying damage locations in an idealized steel bridge girder using the ambient vibration measurements. A sensitive damage feature is proposed in the context of statistical pattern recognition to address the damage detection problem. The study utilizes an experimental program that consists of a two-span continuous steel beam subjected to ambient vibrations. The vibration responses of the beam are measured along its length under simulated ambient vibrations and different healthy/damage conditions of the beam. The ambient vibration is simulated using a hydraulic actuator, and damages are induced by cutting portions of the flange at two locations. Multivariate vector autoregressive models were fitted to the vibration response time histories measured at the multiple sensor locations. A sensitive damage feature is proposed for identifying the damage location by applying Mahalanobis distances to the coefficients of the vector autoregressive models. A linear discriminant criterion was used to evaluate the amount of variations in the damage features obtained for different sensor locations with respect to the healthy condition of the beam. The analyses indicate that the highest variations in the damage features were coincident with the sensors closely located to the damages. The presented method showed a promising sensitivity to identify the damage location even when the induced damage was very small. [Copyright &y& Elsevier]

Abstract: Time–frequency analysis, including the wavelet transform, is one of the new and powerful tools in the important field of structural health monitoring, using vibration analysis. Commonly-used signal analysis techniques, based on spectral approaches such as the fast Fourier transform, are powerful in diagnosing a variety of vibration-related problems in rotating machinery. Although these techniques provide powerful diagnostic tools in stationary conditions, they fail to do so in several practical cases involving non-stationary data, which could result either from fast operational conditions, such as the fast start-up of an electrical motor, or from the presence of a fault causing a discontinuity in the vibration signal being monitored. Although the short-time Fourier transform compensates well for the loss of time information incurred by the fast Fourier transform, it fails to successfully resolve fast-changing signals (such as transient signals) resulting from non-stationary environments. To mitigate this situation, wavelet transform tools are considered in this paper as they are superior to both the fast and short-time Fourier transforms in effectively analyzing non-stationary signals. These wavelet tools are applied here, with a suitable choice of a mother wavelet function, to a vibration monitoring system to accurately detect and localize faults occurring in this system. Two cases producing non-stationary signals are considered: stator-to-blade rubbing, and fast start-up and coast-down of a rotor. Two powerful wavelet techniques, namely the continuous wavelet and wavelet packet transforms, are used for the analysis of the monitored vibration signals. In addition, a novel algorithm is proposed and implemented here, which combines these two techniques and the idea of windowing a signal into a number of shaft revolutions to localize faults. [Copyright &y& Elsevier]

Abstract: The application of Acoustic Emission (AE) technology for machine health monitoring is gaining ground as power tool for health diagnostic of rolling element bearing. This paper provides an investigation that compares the applicability of AE and vibration technologies in monitoring a naturally degraded roller bearing. This research is the first known attempt investigating the comparative effectiveness of applying the Kurtogram to both vibration and AE data from a defective bearing. [Copyright &y& Elsevier]

Abstract: The accuracy of fault diagnostic systems for diesel engine-type generators relies on a comparison of the currently extracted sensory features with those captured during normal operation or the so-called “baseline.” However, the baseline is not easily obtained without the required expertise. Even worse, in an attempt to save costs, many of the diesel engine generators in manufacturing plants are second hand or have been purchased from unknown suppliers, meaning that the baseline is unknown. In this paper, a novel vibration-based fault diagnostic method is developed to identify the vital components of a diesel engine that have abnormal clearance. The advantage of this method is that it does not require the comparison of current operating parameters to those collected as the baseline. First, the nominal baseline is obtained via theoretical modeling rather than being actually captured from the sensory signals in a healthy condition. The abnormal clearance is then determined by inspecting the timing of impacts created by the components that had abnormal clearance during operation. To detect the timing of these impacts from vibration signals accurately, soft-re-sampling and empirical mode decomposition (EMD) techniques are employed. These techniques have integrated with our proposed ranged angle (RA) analysis to form a new ranged angle-empirical mode decomposition method (RA-EMD). To verify the effectiveness of the RA-EMD in detecting the impacts and their times of occurrence, their induced vibrations are collected from a series of generators under normal and faulty engine conditions. The results show that this method is capable of extracting the impacts induced by vibrations and is able to determine their times of occurrence accurately even when the impacts have been overwhelmed by other unrelated vibration signals. With the help of the RA-EMD, clearance-related faults, such as incorrect open and closed valve events, worn piston rings and liners, etc., become detectable even without the comparison to the baseline. Hence, proper remedies can be applied to defective diesel engines to ensure that valuable fuel is not wasted due to the incorrect timing of combustion as well as unexpected fatal breakdown, which may cause loss of production or even human casualties, can be minimized. [Copyright &y& Elsevier]

In this paper the author has introduced transcendental eigenvalue problems for estimating the damage parameters in the continuous structure from measured eigenvalues or natural frequencies. For simplicity axially vibrating rods are considered in which single or multiple damage parameters due to open cracks or notches are simulated by linear springs that are representative of the loss in stiffness or axial rigidity at the location of cracks. Transcendental eigenvalue problems (direct and inverse) associated with rod having multiple damage parameters have been formulated and solved. The numerical method for solving such eigenvalue problems are developed which overcomes the requirements of closed form characteristic frequency equations that are often unavailable. The modeling and solution approach developed here is utilized for evaluating the spectrum of rods with multiple damage parameters as well as for identifying the locations and severity of the damage parameters purely from the eigenvalues. Numerical examples and simulations corresponding to various damage configurations are presented and verified against experimental evidence. It is demonstrated that the solution of transcendental inverse eigenvalue problems can be successfully used for estimating the damage parameters by using only few and selected eigenvalues corresponding to the measured resonant and anti-resonant frequencies of the rod. [Copyright &y& Elsevier]

When we talk about “smart structures” we can think about different properties and capabilities which make a structure “intelligent” in a certain sense. Originally, the expression “smart” was used in the context that a structure can react and adapt to certain environmental conditions, such as change of shape, compensation of deformations, active vibration damping, etc. Over the last year, the expression “smart” has been extended to the field of structural health monitoring (SHM), where sensor networks, actuators and computational capabilities are used to enable a structure to perform a self-diagnosis with the goal that this structure can release early warnings about a critical health state, locate and classify damage or even to forecast the remaining life-time. This paper intends to give an overview and point out recent developments of vibration-based methods for SHM. All these methods have in common that a structural change due to a damage results in a more or less significant change of the dynamic behavior. For the diagnosis an inverse problem has to be solved. We discuss the use of modal information as well as the direct use of forced and ambient vibrations in the time and frequency domain. Examples from civil and aerospace engineering as well as off-shore wind energy plants show the applicability of these methods. [Copyright &y& Elsevier]