Online Structural Health Monitoring Research Articles

Damage detection based on system identification of concrete dams using an extended finite element–wavelet transform coupled procedure

The aim of the present study is to propose a procedure for seismic cracking identification of concrete dams using a coupling of the extended finite element method (XFEM) based on cohesive crack segments (XFEM-COH) and continuous wavelet transform (CWT). First, the dam is numerically modeled using the traditional finite element method (FEM). Then, cracking capability is added to the dam structure by applying the XFEM-COH for concrete material. The results of both the methods under the seismic excitation have been compared and identified to damage detection purposes. In spite of predefined damage in some of the structural health monitoring (SHM) techniques, there is an advantage in the XFEM model where the whole dam structure is potentially under damage risk without initial crack, and may not crack at all. Finally, in order to evaluate any change in the system, that is, specification of any probable crack effects and nonlinear behavior, the structural modal parameters and their variation have been investigated using system identification based on the CWT. The results show that the extended finite element–wavelet transform procedure has high ability for the online SHM of concrete dams that by analysis of its results, the history of physical changes, cracking initiation time, and exact damage localization have been performed from comparing the intact (FEM) and damaged (XFEM) modal parameters of the structural response. In addition, any small change in the system is observable while the final crack profile and performance simulation of the dam body under strong seismic excitations have obtained.

Ultrasonic Inspection and Data Analysis of Glass- and Carbon-Fibre-Reinforced Plastics

Non-destructive testing (NDT) helps to find material defects without having an influence on the material itself. It is applied as a method of quality control, for online structural health monitoring, and for inspection of safety related components. Due to the ability of automation and a simple test setup ultrasonic testing is one major NDT technique next to several existing options. Whereas contact technique allows the use of higher frequencies of some MHz and phased array focusing, air-coupled ultrasonic testing (ACUT) shows different advantages. Most significant for ACUT is the absence of any coupling fluid and an economical test procedure respective time and costs. Both contact technique and ACUT have been improved and enhanced during the past years. One important enhancement is the development of airborne transducers based on ferroelectrets, like charged cellular polypropylene (cpp), which makes the application of any matching layers being mandatory in conventional piezoelectric transducers unnecessary. In this contribution we show ultrasonic inspection results of specimens made of carbon- and glass-fibre-reinforced plastic. These specimens include defects represented by drill holes and artificial delaminations of various size and depth. We compare inspection results achieved by using contact technique to those achieved by ACUT. For ACUT, conventional piezoelectric transducers and transducers based on cpp were used, both focused as well as non-focused types. Contact inspections were performed with a multi-channel matrix array probe. Once the inspection data is recorded it can be analysed in order to detect and evaluate defects in the specimen. We present different analysing strategies and compare these regarding detection rate and sizing of defects.

On guided circumferential waves in soft electroactive tubes under radially inhomogeneous biasing fields

Soft electroactive (EA) tube actuators and many other cylindrical devices have been proposed recently in literature, which show great advantages over those made from conventional hard solid materials. However, their practical applications may be limited because these soft EA devices are prone to various failure modes. In this paper, we present an analysis of the guided circumferential elastic waves in soft EA tube actuators, which has potential applications in the in-situ nondestructive evaluation (NDE) or online structural health monitoring (SHM) to detect structural defects or fatigue cracks in soft EA tube actuators and in the self-sensing of soft EA tube actuators based on the concept of guided circumferential elastic waves. Both circumferential SH and Lamb-type waves in an incompressible soft EA cylindrical tube under inhomogeneous biasing fields are considered. The biasing fields, induced by the application of an electric voltage difference to the electrodes on the inner and outer cylindrical surfaces of the EA tube in addition to an axial pre-stretch, are inhomogeneous in the radial direction. Dorfmann and Ogden's theory of nonlinear electroelasticity and the associated linear theory for small incremental motion constitute the basis of our analysis. By means of the state-space formalism for the incremental wave motion along with the approximate laminate technique, dispersion relations are derived in a particularly efficient way. For a neo-Hookean ideal dielectric model, the proposed approach is first validated numerically. Numerical examples are then given to show that the guided circumferential wave propagation characteristics are significantly affected by the inhomogeneous biasing fields and the geometrical parameters. Some particular phenomena such as the frequency veering and the nonlinear dependence of the phase velocity on the radial electric voltage are discussed. Our numerical findings demonstrate that it is feasible to use guided circumferential elastic waves for the ultrasonic non-destructive online SHM to detect interior structural defects or fatigue cracks and for the self-sensing of the actual state of the soft EA tube actuator.

A new magnetic flux leakage sensor based on open magnetizing method and its on-line automated structural health monitoring methodology

Magnetic flux leakage sensors have been widely used for the structural health monitoring of ferrous materials (structural health monitoring). This article provides a new magnetic flux leakage sensor based on open magnetizing method. Starting with the traditional yoke magnetization method analyses, the provided open magnetizing method and testing principle are presented. Then, the comparative analyses between the two methods are conducted by finite element method, indicating that the open magnetizing method features less magnetic interaction force, more uniform magnetic flux density for swing scanning, simpler architectures, and more universal functions under the framework of similar magnetic excitation capability for defects. Furthermore, the sensor based on open magnetizing method is designed through optimization and the advantages for open magnetization method are confirmed by experiments with created sensors. Finally, on-line structural health monitoring methodology for the monitoring probe with “rigid” open–close and “free” tracking rope-swing functions is developed based on the proposed sensors. Meanwhile, experimental comparisons between the open and yoke probes for on-line automated monitoring are also conducted to successfully confirm the characters of smaller magnetic interaction force, less wear, and damage in contrast to the traditional on-line automated structural health monitoring technology. Additionally, the on-line automated magnetic flux leakage apparatus for the structural health monitoring of hoist wire ropes is developed and applied, which further confirms the good practicability for the structural health monitoring of hoist ropes under the poor working conditions.

Statistical enhancement of a dynamic equilibrium-based damage identification strategy: Theory and experimental validation

A previously developed damage identification strategy, named Pseudo-Excitation (PE), was enhanced using a statistical processing approach. In terms of the local dynamic equilibrium of the structural component under inspection, the distribution of its vibration displacements, which are of necessity to construct the damage index in the PE, was re-defined using sole dynamic strains based on the statistical method. On top of those advantages inheriting from the original PE compared with traditional vibration-based damage detection including the independence of baseline signals and pre-developed benchmark structures, the enhanced PE (EPE) possesses improved immunity to the interference of measurement noise. Moreover, the EPE can facilitate practical implementation of online structural health monitoring, benefiting from the use of sole strain information. Proof-of-concept numerical study was conducted to examine the feasibility and accuracy of the EPE, and the effectiveness of the proposed statistical enhancement in re-constructing the vibration displacements was evaluated under noise influence; experimental validation was followed up by characterizing multi-cracks in a beam-like structure, in which the dynamic strains were measured using Lead zirconium titanate (PZT) sensors. For comparison, the original PE, the Gapped Smoothing Method (GSM), and the EPE were respectively used to evaluate the cracks. It was observed from the damage identification results that both the GSM and EPE were able to achieve higher identification accuracy than the original PE, and the robustness of the EPE in damage identification was proven to be superior than that of the GSM.

Probabilistic-based damage identification based on error functions with an autofocusing feature

This study presents probabilistic-based damage identification technique for highlighting damage in metallic structures. This technique utilizes distributed piezoelectric transducers to generate and monitor the ultrasonic Lamb wave with narrowband frequency. Diagnostic signals were used to define the scatter signals of different paths. The energy of scatter signals till different times were calculated by taking root mean square of the scatter signals. For each pair of parallel paths an error function based on the energy of scatter signals is introduced. The resultant error function then is used to estimate the probability of the presence of damage in the monitoring area. The presented method with an autofocusing feature is applied to aluminum plates for method verification. The results identified using both simulation and experimental Lamb wave signals at different central frequencies agreed well with the actual situations, demonstrating the potential of the presented algorithm for identification of damage in metallic structures. An obvious merit of the presented technique is that in addition to damages located inside the region between transducers; those who are outside this region can also be monitored without any interpretation of signals. This novelty qualifies this method for online structural health monitoring.

Damage detection with streamlined structural health monitoring data.

The huge amounts of sensor data generated by large scale sensor networks in on-line structural health monitoring (SHM) systems often overwhelms the systems’ capacity for data transmission and analysis. This paper presents a new concept for an integrated SHM system in which a streamlined data flow is used as a unifying thread to integrate the individual components of on-line SHM systems. Such an integrated SHM system has a few desirable functionalities including embedded sensor data compression, interactive sensor data retrieval, and structural knowledge discovery, which aim to enhance the reliability, efficiency, and robustness of on-line SHM systems. Adoption of this new concept will enable the design of an on-line SHM system with more uniform data generation and data handling capacity for its subsystems. To examine this concept in the context of vibration-based SHM systems, real sensor data from an on-line SHM system comprising a scaled steel bridge structure and an on-line data acquisition system with remote data access was used in this study. Vibration test results clearly demonstrated the prominent performance characteristics of the proposed integrated SHM system including rapid data access, interactive data retrieval and knowledge discovery of structural conditions on a global level.

Active and passive monitoring of valve bodies utilizing spray-on transducer technology

Structural health monitoring (SHM) and non-destructive evaluation (NDE) can be performed both actively and passively. Active monitoring is useful for thickness measurement and crack interrogation. Passive monitoring can indicate integrity of the valve and if it is open or closed. Traditional SHM methods for valve bodies require a bonding medium that can deteriorate. A sol-gel spray-on technology eliminates the need for a coupling medium since the transducer is chemically bonded to the valve body. These spray-on transducers can be tailored to specific applications in order to maximize response or operating temperature. This technology allows for efficient on-line monitoring of valve bodies for thickness and valve integrity. The current objective is to develop a spray-on transducer and corresponding method for both active and passive SHM of valve bodies. The objective relating specifically to passive monitoring relates to indicating the condition of the valve and determining what position it is in. The active monitoring goals are to measure the thickness of the valve in critical regions, determine the presence of corrosion, and observe the roughness of the interior surface. This paper provides preliminary results on the use of spray-on transducers for on-line SHM of valve bodies for both active and passive applications.

Assessment of Fatigue Behaviour of Orthotropic Steel Bridge Decks using Monitoring System

During the last decades orthotropic steel decks have become a widely used component of steel bridges, however high numbers of fatigue cracks have been recently detected in these structure types. It is well known, that the orthotropic decks are sensitive to fatigue damage, therefore safe fatigue design is important for the safety assessment of bridge structures. The fatigue damage assessment and life prediction of steel bridges can be carried out by using online structural health monitoring systems (SHM). By using the SHM system, it is possible to detect deterioration, determine anomalies and assess the safety level which will allow optimum maintenance strategies. On-structure long-term monitoring systems have been already successfully implemented on bridges all around the world in order to detect cracks just like on the orthotropic steel deck structure of M0 Háros Danube highway Bridge in Hungary. The purpose of the current research program is the investigation and analysis of the fatigue behaviour of this orthotropic steel bridge deck and the evaluation process improvement of the data rows collected by the monitoring system. Additional laboratory tests are carried out at 6 large scale test specimens at the Budapest University of Technology and Economics, Department of Structural Engineering to determine the fatigue resistance of closed section longitudinal stiffeners. In parallel finite element model is developed to simulate the fatigue behaviour of the analyzed components and to investigate its fatigue behaviour by numerical analysis. The aim of the current study is to analyze and verify the structural behaviour of the orthotropic steel deck on the basis of the executed test results, numerical simulations and the strain-time history data from the online structural health monitoring system implemented on the studied bridge.

Signal Processing Techniques for Vibration-Based Health Monitoring of Smart Structures

Signal processing is the key component of any vibration-based structural health monitoring (SHM). The goal of signal processing is to extract subtle changes in the vibration signals in order to detect, locate and quantify the damage and its severity in the structure. This paper presents a state-of-the-art review of recent articles on signal processing techniques for vibration-based SHM. The focus is on civil structures including buildings and bridges. The paper also presents new signal processing techniques proposed in the past few years as potential candidates for future SHM research. The biggest challenge in realization of health monitoring of large real-life structures is automated detection of damage out of the huge amount of very noisy data collected from dozens of sensors on a daily, weekly, and monthly basis. The new methodologies for on-line SHM should handle noisy data effectively, and be accurate, scalable, portable, and efficient computationally.

Damage identification based on response-only measurements using cepstrum analysis and artificial neural networks

This article presents a response-only structural health monitoring technique that utilises cepstrum analysis and artificial neural networks for the identification of damage in civil engineering structures. The method begins by applying cepstrum-based operational modal analysis, which separates source and transmission path effects to determine the structure’s frequency response functions from response measurements only. Principal component analysis is applied to the obtained frequency response functions to reduce the data size, and structural damage is then detected using a two-stage ensemble of artificial neural networks. The proposed method is verified both experimentally and numerically using a laboratory two-storey framed structure and a finite element representation, both subjected to a single excitation. The laboratory structure is tested on a large-scale shake table generating ambient loading of Gaussian distribution. In the numerical investigation, the same input is applied to the finite model, but the obtained responses are polluted with different levels of white Gaussian noise to better replicate real-life conditions. The damage is simulated in the experimental and numerical investigations by changing the condition of individual joint elements from fixed to pinned. In total, four single joint changes are investigated. The results of the investigation show that the proposed method is effective in identifying joint damage in a multi-storey structure based on response-only measurements in the presence of a single input. Because the technique does not require a precise knowledge of the excitation, it has the potential for use in online structural health monitoring. Recommendations are given as to how the method could be applied to the more general multiple-input case.

Effective Models of PZT Actuators for Numerical Simulation of Elastic Wave Propagation

In this study, to accurately identify the functions of piezoelectric actuators and sensors for the generation and collection of elastic waves in typical engineering structures, several effective models of surface-bounded flat PZT disks are further developed and validated for numerical modelling of elastic wave propagations. Based on these models, a series of finite element models of elastic waves in plates are devised using both implicit and explicit dynamics analysis techniques and those numerical simulations are conducted and verified one another. The results flowed from the present research is being used to study the elastic wave propagation in pipes and develop an online structural health monitoring (SHM) system with an integrated piezoelectric actuator-sensor network.

Neural network approach for damaged area location prediction of a composite plate using electromechanical impedance technique

Abstract Nowadays, breakthrough composite technologies are intensifying the complexity of structural components every day and assuring the structural integrity is becoming more essential, thus creating challenges for developing a cost effective and reliable non-destructive evaluation (NDE) technique. As conventional NDE techniques usually require expensive equipments, trained experts and out-of-service period, such techniques may be inadequate for autonomous online health monitoring of structures. In this study, a relatively new technique known as electromechanical impedance (EMI) technique is combined with a neural network technique to predict the damaged areas on a composite plate. Regardless of the advantages such as low cost, robustness, simplicity and online possibilities, this technique still has various problems to be solved. For one, locating a damaged area can be extremely difficult as this non-model based technique heavily relies on the variations in the impedance signatures. The results show that the non-homogenous property is an advantage for the study, successfully identifying the damage location for the prepared test specimen with an acceptable performance.

Fast damage imaging using the time-reversal technique in the frequency–wavenumber domain

The time-reversal technique has been successfully used in structural health monitoring (SHM) for quantitative imaging of damage. However, the technique is very time-consuming when it is implemented in the time domain. In this paper, we study the technique in the frequency–wavenumber (f–k) domain for fast real-time imaging of multiple damage sites in plates using scattered flexural plate waves. Based on Mindlin plate theory, the time reversibility of dispersive flexural waves in an isotropic plate is theoretically investigated in the f–k domain. A fast damage imaging technique is developed by using the cross-correlation between the back-propagated scattered wavefield and the incident wavefield in the frequency domain. Numerical simulations demonstrate that the proposed technique cannot only localize multiple damage sites but also potentially identify their sizes. Moreover, the time-reversal technique in the f–k domain is about two orders of magnitude faster than the method in the time domain. Finally, experimental testing of an on-line SHM system with a sparse piezoelectric sensor array is conducted for fast multiple damage identification using the proposed technique.

Non-reflecting boundary condition for Lamb wave propagation problems in honeycomb and CFRP plates using dashpot elements

The paper’s objective is to introduce a new non-reflecting boundary condition using dashpot elements. This is an useful tool to efficiently simulate Lamb wave propagation within composite structures, such as honeycomb and CFRP plates. Due to the steadily increasing interest in applying Lamb waves in modern online structural health monitoring techniques, several numerical and experimental studies have been carried out recently. The proposed boundary condition poses the advantage of reducing the computational costs required to simulate the wave propagation in heterogenous materials. Different parameters which can influence the functionality of such an artificial boundary are discussed and several applications are presented. Finally, the results are also experimentally validated.

Validation of algorithms for delamination detection in composite structures using experimental data

This research work deals with aspects concerned with delamination detection in composite structures as revealed by an approach based on vibration measurements. Variations in vibration characteristics generated in composite laminates indicate the existence of delaminations because degradation due to delamination causes reduction in flexural stiffness and strength of the material and as a result vibration parameters like natural frequency responses are changed. Hence, it is possible to monitor the variation in natural frequencies to identify the presence of delamination, and assess its size and location for online structural health monitoring (SHM). The approach to this paper, therefore, typically depends on undertaking the analysis of structural models implemented by finite element analysis (FEA). The numerical solutions using FE models known as the simulator computes the natural frequencies for the delaminated and undelaminated specimens of composite laminates. However, these FE models are computationally expensive, and surrogate (approximation) models are introduced to curtail the computational expense. The simulator is employed to solve the inverse problem using algorithms based on computational intelligence concepts. An artificial neural network (ANN) model is developed to also solve the inverse problem for delamination detection directly and to provide surrogate models integrated with optimization algorithms (the gradient-based local search and non-dominated sorting genetic algorithm-II) to contain the computationally expensive simulations by FEA. This approach is termed as surrogate assisted optimization and it is seen that the engagement of surrogate models in lieu of the FE models in the optimization loop greatly enhances the accuracy of delamination detection results within an affordable computational cost and provides control over handling different variables. Meanwhile, to aid with the building of effective surrogate models using substantial number of training datasets, K-means clustering algorithm is harnessed and this effectively reduces the large training datasets usually required for ANN training. This paper demonstrated that ANN and optimization algorithms with surrogates show immense potentialities for use in delamination damage detection scenarios. Prediction errors of the algorithms were quantified and they were shown to be satisfactory when applied to previously experimental data. The algorithms in their inverse formulations are capable of predicting accurately delamination parameters. Hence, these algorithms should be employed for application in the domain of SHM where their small computational requirements could be exploited for online damage detection.

Extreme value theory-based structural health prognosis method using reduced sensor data

This paper presents an extreme value theory (EVT)-based structural health prognosis method that can be used for estimating the quantile values of remaining useful life (RUL) of monitored structure with reduced sensor data. Massive sensor data generated from online structural health monitoring system can be utilised to provide more refined prognosis results such as statistical distribution of RULs. By using the moment estimator from EVT, only a small portion of the full sensor data-set is actually used for estimating the quantile values of the RUL. This can considerably cut the computing time required for structural health prognosis. As a requirement for implementing the EVT-based prognosis, monotonicity relation between damage index (either measured or derived from sensor data) and RUL values has to be satisfied though. Common prognosis problems in civil engineering include fatigue cracking in steel structures and steel rebar corrosion in reinforced concrete structures. To illustrate the EVT-based prognosis, the monotonicity condition is shown for the selected degradation models in two case studies involving fatigue life estimation and pitting corrosion life of steel reinforcing rebar, respectively. The results show that EVT-based structural health prognosis method is computationally efficient without loss of much accuracy.

Influence of the cover plate thickness on the Lamb wave propagation in honeycomb sandwich panels

Within this paper, the guided Lamb wave propagation in thin honeycomb sandwich panels is studied. The Lamb waves are excited by thin piezoelectric (PZT) patch actuators glued to the surface of the plate, and the signals are received by similar PZT sensor patches. Such actuator and sensor systems can be used for a cost-effective online health monitoring of structures. In homogeneous plates, Lamb waves propagate with symmetrical and anti-symmetrical modes. However, the propagation in heterogeneous media is not as clear and depends on the exciting frequency, the material properties, and the geometry of the structure. In this paper, the influence of the geometrical properties of honeycomb plates on the Lamb wave propagation is studied. For this purpose detailed 3-D finite element calculations are performed, which result in very time consuming computations. Consequently, also simplified models are developed to reduce the computing time without losing the quality of the results. To this end, the honeycomb core material is replaced by a homogeneous layer with orthotropic mechanical properties. The homogenized properties are calculated numerically using a homogenization technique based on the representative volume element method. The comparison of the results received with the two different approaches has shown that the simplified models are in a good agreement with the extended models for a certain range of exciting frequencies and geometrical properties only. The wave propagation on the top and bottom surfaces is also compared in order to show how deep the waves can travel inside the structure.

Integrated ARMA model method for damage detection of subsea pipeline system

An integrated ARMA model algorithm is developed in this study for the structural health monitoring (SHM) of subsea pipeline system. In which, the partition and normalization procedure is firstly employed in signal pre-processing to remove the influence of various loading conditions, the auto-correlation function of the normalized signal is utilized as a substitute of analysis input to overcome noise effect and avoid the bias in Autoregressive Moving Average (ARMA) model fitting caused by noise disturbance as well. Then, Partial Auto-correlation Function (PAF) method is employed in building optimal ARMA model. With which Autoregressive (AR) parameters serving as damage feature vector, a damage indicator (DI) based on the Mahalanobis distance between ARMA models is defined for damage detection and localization. Dynamic vibration of subsea pipeline system under ambient excitations is numerically simulated in ANSYS software and the acceleration responses of pipeline in various damage cases are analyzed utilizing the proposed integrated method. In numerical study, the finite element (FE) model of a subsea soil-pipeline-fluid system is developed and the undersea hydrodynamic force acting on the pipeline is derived based on Spectral Analysis Method. Finally, the proposed method is proved to be robust and very sensitive to damage. It provides accurate identification of damage existence and damage locations with high time efficiency. Therefore, it can be used for the online SHM of subsea pipeline structures and other civil structures.

Numerical simulation of the Lamb wave propagation in honeycomb sandwich panels: A parametric study

The paper aims to describe the guided Lamb wave propagation in honeycomb sandwich panels. The application of Lamb waves is a well-known method in modern online structural health monitoring techniques. To analyze the wave propagation in such a complicated geometry with an analytical solution is hardly possible; therefore a dimensional finite element simulation is used. A piezoelectric actuator is used to excite the waves on the sandwich panel surface. The extended model of the honeycomb sandwich panel is consisting of two plate layers and a mid-core layer. In addition, a simplified model is used to reduce the computing costs, where the mid-core of the sandwich panel is replaced by a homogeneous layer. The results from the extended model and the simplified model are in most cases in a good agreement; however the limitations of using the simplified model are discussed. A parametric study is used to show the influence of the geometrical properties of honeycomb plates, the material properties of the skin plates and the loading frequency on the group velocity, the wave length and the energy transmission. Each of these properties provides valuable information to design an efficient health monitoring system. Finally, an experimental test is presented.