Local Damage Detection Research Articles

Damage identification by multi-model updating in the modal and in the time domain

Computational model updating techniques are used to adjust selected parameters of finite element models in order to make the models compatible with experimental data. This is done by minimizing the differences (residuals) of analytical and experimental data, for example, natural frequencies and mode shapes by numerical optimization procedures. For a long-time updating techniques have also been investigated with regard to their ability to localize and quantify structural damage. The success of such an approach is mainly governed by the quality of the damage model and its ability to describe the structural property changes due to damage in a physical meaningful way. Our experience has shown that due to unavoidable modelling simplifications and measurement errors the changes of the corresponding damage parameters do not always indicate structural modifications introduced by damage alone but indicate also the existence of other modelling uncertainties which may be distributed all over the structure. This means that there are two types of parameters which have to be distinguished: the damage parameters and the other parameters accounting for general modelling and test data uncertainties. Although these general parameters may be physically meaningless they are necessary to achieve a good fit of the test data and it might happen that they cannot be distinguished from the damage parameters. For complex industrial structures it is seldom possible to generate unique structural models covering all possible damage scenarios so that one has to expect, that the parameters introduced for describing the damage will not be fully consistent with the physical reality. Even then the change of such parameters identified from test data taken continuously or temporarily over the time may serve as a feature for structural health monitoring. It is well known that low-frequency modal test data or static response data are not very well suited for detecting and quantifying localized small size damage. Time domain response data from impact tests carry high-frequency information which usually is lost when experimental modal data are utilized for damage identification. Even so only little literature was found addressing the utilization of experimental time histories for model updating in conjunction with damage identification. In the present paper we summarize the methodology of computational model updating and report about our experience with damage identification using two different model updating techniques. The first is based on classical modal residuals (natural frequencies and mode shapes) which is extended to allow for simultaneous updating of two models, one for the initial undamaged structure and the second for the damaged structure using the test data of both states (multi-model updating). The second technique uses residuals composed of measured and analytical time histories. Time histories have the advantage of carrying high-frequency information which is beneficial for the detection of local damage and which usually is lost when modal residuals are used. Both techniques have been applied to the same beam structure consisting of two thin face sheets which were bonded together by an adhesive layer. It was the aim of this application to study the performance of the two techniques to localize and quantify the damage which was introduced locally in the adhesive layer.

Optimal filtering of gear signals for early damage detection based on the spectral kurtosis

In this paper, we propose a methodology for the enhancement of small transients in gear vibration signals in order to detect local tooth faults, such as pitting, at an early stage of damage. We propose to apply the optimal denoising (Wiener) filter based on the spectral kurtosis (SK). The originality is to estimate and apply this filter to the gear residual signal, as classically obtained after removing the mesh harmonics from the time synchronous average (TSA). This presents several advantages over the direct estimation from the raw vibration signal: improved signal/noise ratio, reduced interferences from other stages of the gearbox and easier detection of excited structural resonance(s) within the range of the mesh harmonic components. From the SK-based filtered residual signal, called SK-residual, we define the local power as the smoothed squared envelope, which reflects both the energy and the degree of non-stationarity of the fault-induced transients. The methodology is then applied to an industrial case and shows the possibility of detection of relatively small tooth surface pitting (less than 10%) in a two-stage helical reduction gearbox. The adjustment of the resolution for the SK estimation appears to be optimal when the length of the analysis window is approximately matched with the mesh period of the gear. The proposed approach is also compared to an inverse filtering (blind deconvolution) approach. However, the latter turns out to be more unstable and sensitive to noise and shows a lower degree of separation, quantified by the Fisher criterion, between the estimated diagnostic features in the pitted and unpitted cases. Thus, the proposed optimal filtering methodology based on the SK appears to be well adapted for the early detection of local tooth damage in gears.

The Effect of Prestress on Vibration Behavior of Structures in Impedance Method Damage Detection

Structural health monitoring or damage detection has long been a research interest for its great potential for life safety and economic benefits to the industrialized world. Structural vibration behavior is an essential signature of the integrity of structures and hence has been used for damage detection. Structural vibration impedance by way of piezoceramic patch excitation offers a local damage detection technique. It has been known that temperature change has adverse effects on the measured impedance result and can complicate the damage analysis. It is believed that one way of temperature influence on vibration is through adding thermal prestress to the structure. Prestress affects vibration in different ways on different structures and application problems. For the impedance method, prestress comes not only from temperature change but also from other sources such as wind, gravity and working load. This paper deals with prestress effects in the context of local vibration behavior of structures. A theoretical analysis is given on how prestress affects the vibration. Experimental impedance measurement results for piezoceramic patch excited vibration of simple structures such as plates under prestress are presented.

STRUCTURAL HEALTH MONITORING ORIENTED FINITE ELEMENT MODEL OF TSING MA BRIDGE TOWER

The modeling, updating and validation of a structural health monitoring oriented finite element model (FEM) of the Tsing Ma suspension bridge towers are presented in this paper. The portal-type bridge tower is composed of two hollow reinforced concrete legs and four deep pre-stressed cross-beams with a steel truss cast in the concrete of each cross-beam to form a narrow corridor for access between two legs. Except that steel trusses are modeled by beam elements, all structural components are modeled by solid elements to facilitate local damage detection, in particular at member joints. The established tower model is then updated using sensitivity-based model updating method taking the natural frequencies identified from field measurement data as reference. Furthermore, a two-level validation criterion is proposed and implemented to examine the replication performance of the updated finite element model of the bridge tower in terms of (1) natural frequencies in higher modes of vibration and (2) dynamic characteristics of the tower-cable system. The validation results show that a good replication of dynamic characteristics is achieved by the updated tower model when compared to the field measurement results. Finally, stress distribution and concentration of the bridge tower are investigated through nonlinear static analysis of the tower-cable system.

Damage localisation and severity evaluation of a beam-like timber structure based on modal strain energy and flexibility approaches

Timber bridges are often exposed to harsh environmental conditions, which, over time, can lead to deterioration resulting from decay, insect attack, weathering, and mechanical damage. In turn, this deterioration may lead to a loss of structural integrity that is detrimental to the structure and its users. Non-destructive evaluation (NDE) techniques provide effective means for assessment of current structural integrity status of given structures. As a global NDE technique, vibration-based damage identification techniques have received increasing attention for structural damage evaluation. Among them, there is a popular and promising damage identification method called the damage index method based on calculating the change of modal strain energy before and after damage. Despite its popularity, however, a systematic investigation on its capability and limitations in locating and quantifying damage is yet to be reported, especially for structures suffering multiple damage. In this paper, after reviewing the current NDE methods, the focus was set on the vibration-based NDE methods, in particular the damage index method. The investigation was based on finite element analysis (FEA) results of a timber beam by which the capability and limitation of the damage index was fully explored. As a result of the investigation, a new hybrid algorithm combining modified damage index (MDI) and change in flexibility algorithms is proposed for damage localisation and severity evaluation. The numerical results indicate that the proposed hybrid algorithms based on MDI method and flexibility have clear advantages in detecting damage locations and evaluating damage severities, especially for multiple damage cases.

Genetic fuzzy system for online structural health monitoring of composite helicopter rotor blades

A structural health monitoring (SHM) methodology is developed for composite rotor blades. An aeroelastic analysis of composite rotor blades based on the finite element method in space and time and with implanted matrix cracking and debonding/delamination damage is used to obtain measurable system parameters such as blade response, loads and strains. A rotor blade with a two-cell airfoil section and [ 0 / ± 45 / 90 ] s family of laminates is used for numerical simulations. The model based measurements are contaminated with noise to simulate real data. Genetic fuzzy systems (GFS) are developed for global online damage detection using displacement and force-based measurement deviations between damaged and undamaged conditions and for local online damage detection using strains. It is observed that the success rate of the GFS depends on number of measurements, type of measurements and training and testing noise level. The GFS work quite well with noisy data and is recommended for online SHM of composite helicopter rotor blades.

Application of the short-time Fourier transform and the two-dimensional Fourier transforms to detect damage in plates

Lamb waves are widely used in the fields of characterization and nondestructive testing of structures. Usually, the full characterization of the structures requires two kinds of information: the first one is related to the material quality, the other one aims at the evaluation of the structure health, i.e., the detection of localized damage or defects. Time-frequency analyses or two-dimensional Fourier transforms (2DFT) have been used separately to solve such a problem. The purpose in this work is therefore to present a dual signal processing approach based on the relationship between the STFT (short-time Fourier transform) and the 2DFT amplitudes that allows a better interpretation and identification of Lamb modes. This relationship will be numerically verified by a two-dimensional finite-element method. Experimentally, a 3-mm-thick aluminum plate with an emitter and a receiver are considered. The emitter consists of two piezoelectric transducers while the receiver consists of a 32 multi-electrode piezoelectric transducer. This receiver has been developed especially to increase signal-to-noise ratio at the reception. In order to simulate damages a hole with different diameter was introduced in the plate center. The application of this dual signal processing has located conversion modes phenomenon.

A statistical algorithm for comparing mode shapes of vibration testing before and after damage in timbers

Instances of local damage in timber such as knots, decay, and cracks can be translated into a reduction of service life due to mechanical and environmental loadings. In wood construction, it is very important to evaluate the weakest location and to detect damage at the earliest possible stage to avoid future catastrophic failure. In this study, modal testing was used on wood beams to generate the first two mode shapes. A novel statistical algorithm was proposed to extract a damage indicator by computing mode shapes of vibration testing before and after damage in timbers. The different damage severities, damage locations, and damage counts were simulated by removing mass from intact beams to verify the algorithm. The results showed that the proposed statistical algorithm is effective and suitable for the designed damage scenarios. It is reliable for the detection and location of local damage of different severities, location, and number. The peak values of the damage indicators computed from the first two mode shapes were sensitive to different damage severities and locations. They were also reliable for the detection of multiple cases of damage.

On the complexity of artificial neural networks for smart structures monitoring

A Bayesian probabilistic approach is presented for smart structures monitoring (damage detection) based on the pattern matching approach utilizing dynamic data. Artificial neural networks (ANNs) are employed as tools for matching the “damage patterns” for the purpose of detecting damage locations and estimating their severity. It is obvious that the selection of the class of feed-forward ANN models, i.e., the decision of the number of hidden layers and the number of hidden neurons in each hidden layer, has crucial effects on the training of ANNs as well as the performance of the trained ANNs. This paper presents a Bayesian probabilistic method to select the ANN model class with suitable complexity, which is usually overlooked in the literature. An example using a five-story building is used to demonstrate the proposed methodology, which consists of a two-phase damage detection method and a Bayesian ANN design method.

Experimental verification for detection of damage of sensor-arrayed truss domes using afocusing strategy

A damage detection scheme using a focusing strategy, developed for the purpose ofmaintaining structural integrity of truss domes during their service life, that estimates thelocation and extent of damage on the basis of vibration monitoring and systemidentification is presented. The main idea of the focusing strategy is to reduce the numberof unknown physical parameters as much as possible by shifting from global tolocal damage detection gradually. The scheme is divided into three stages: global,intermediate and local damage detection. In the global damage detection, the centralconcern is whether the damage occurs in the superstructure or substructure. Inthe intermediate damage detection, possible damaged regions are located in thesuperstructure. In the local damage detection, damaged members or joints are located inthe damaged regions. Finally, the extent of member, joint or support damageis quantified in terms of member stiffness reduction ratios. Depending on thedamage detection levels, different probing forces are used and different damageindices are defined. To verify the overall damage detection scheme, a series ofshaking table, vibration generator and impact hammer tests have been carried outfor a small-scale experimental model. On the basis of the damage identificationresults, the effectiveness and limitations of the proposed scheme are discussed.

Structural damage diagnosis under varying environmental conditions—part II: local PCA for non-linear cases

It is well known that changes in vibration features of structures due to damage may be masked by the effects of environmental variations. This influence has to be eliminated in the structural health-monitoring process, especially when a long-term in situ monitoring is expected. In the companion paper [1] a linear method based on principal component analysis (PCA) has been proposed and has shown encouraging results for linear or even weakly non-linear cases. The present paper concerns a further extension of the proposed method to handle non-linear cases, which may be encountered in some complex structures. The method involves a two-step procedure, namely a clustering of the data space into several regions and then the application of PCA in each local region. The application of local PCA allows performing a piecewise linearisation of the non-linear problem. A close look at the choice of the distortion function used in data clustering leads to two new clustering strategies. Whereas the first strategy is specifically suitable for the application treated in this paper, the second one is more general. The local PCA-based damage detection method is applied for the structural health monitoring of a real bridge using vibration data measured in situ over a one-year period.

Local damage detection using the two-dimensional gapped smoothing method

This paper presents a procedure for locating variability in structural stiffness. For some types of structure, this variability is directly related to manufacturing defects and/or in-service damage. Unlike many published damage detection methods, the procedure presented here uses only data obtained from the damaged structure. Baseline data and theoretical models of the undamaged structure are not used during the analysis presented here. The procedure locates regions in a structure where the stiffness varies. Providing it is known that the structure, in its undamaged state, is homogeneous with respect to stiffness, the procedure will detect the areas of inhomogeneity that are caused by the incipient damage. For non-homogeneous structures, some knowledge of the structural details (for example, engineering drawings or a baseline test) is required in order to discriminate damage. The procedure is a two-dimensional generalization of a previously published one-dimensional gapped smoothing method, whereby local features in vibration curvature shapes are extracted using a localized curve fit (i.e., smoothing). A variability index is generated for each test point on the structure. Increased variability is due either to structural stiffness features or damage. A statistical treatment of the indices enables discrimination of areas with significant stiffness variability. Providing the damaged areas are sufficiently small compared to the total surface area, their indices will be statistical outliers. The procedure can either analyze mode shape data, or frequency dependent operating displacement shape data. The procedure is demonstrated with a finite element model of a plate, and experiments on composite plates with deliberately induced multiple delaminations. Finally, the method is demonstrated on data taken from a large composite hull structure. In all cases the procedure successfully located the damaged regions.

Serially Multiplexed Dual-Point Fiber-Optic Acoustic Emission Sensor

Development of a serially multiplexed fiber-optic acoustic emission (AE) sensor is described. The sensor detects AE events in real time at two different points along the length of an optical fiber. The time-domain waveform contains signals from the two locations, and a method is introduced for identification of the signals. Detection and identification of signals along a single data stream reduces the data-acquisition rigor and provides for rapid real-time damage location detection in materials. The sensor consists of two optical fiber coils along an arbitrary length in a Michelson interferometer arrangement. The formulations involve the methodology for tagging of the time-domain signals. The process leads to matching of the arrival times in the waveform to the individual coils along the length of the optical fiber. The waveform is detected at the quadrature point for maximum sensitivity. A bias is created through differentiation and superposition of the waveform to accomplish signal identification. The experimental program involved proof of concept tests by way of damage location detection along the lengths of individual composite rods. Conventional piezoelectric transducers were also employed for comparison of results.

Experimental investigation of local damage detection on a 1/15 scale model of a suspension bridge deck

This paper addresses signal processing and applicability of local damage detection algorithms on a 1/15 scale model of the Yeongjong Bridge floor system. As many as 48 channels of dynamic strain data were collected, corresponding to strain gauge placements on the in-situ structure. Model excitation was induced via a series of impact hammer strikes. Twelve different damage cases are examined including connection loosening, chord removal, saw-cuts on the deck plate, and bearing removal simulating hanger failure. Signal anomaly index (SAI) representing the amount of change in frequency response is adopted to detect the presence and type of damage along with a series of neural network classifiers. It is found that SAI and ANN are promising tools for detecting local damage in steel bridges.

DAMAGE DETECTION USING SPATIALLY INCOMPLETE FREQUENCY RESPONSE FUNCTIONS

A damage detection technique based upon an incompletely measured experimental model is studied. Conventional methods such as force balance method are effective to detect damage locations as long as the initial finite element model is accurate and the responses at all the degrees of freedom can be measured experimentally. But these two conditions are practically impossible to be met, with the exception of geometrically simple structures. This work suggests a damage detection method not utilising an accurate analytical finite element model but based upon incompletely measured frequency responses, noting that the reduced dynamical system is an inverse of incompletely measured frequency responses. This work also discusses frequency regions where the suggested method works satisfactorily. From both numeric and test examples, it is found that the proposed method can be an alternative to conventional damage detection methods.

Fuzzy logic expert system for automated damage detection from changes in strain energy mode shapes

In recent years, researchers have developed several methods for damage detection in structures employing strain energy or curvature mode shapes. Experience shows that mode shape methods are highly sensitive to measurement noise. Such sensitivity is a direct result of the second derivative applied to the displacement mode shapes, which produces curvature mode shapes. Calculation of strain energy mode shapes includes calculation of curvature as well. Consequently, the artifact false peaks (normally an indicator of damage) appear on the strain energy mode shapes, reducing accuracy of damage recognition. Combined with the intrinsic variance in damage peak shapes and amplitudes dependent upon magnitude and location of damage, the false peaks create a major problem for automated detection of damage location. Most often, a highly qualified expert is required to analyze the data and to identify and locate damage. In an attempt to replace the human expert with a computer, this paper describes a fuzzy logic expert system designed to mimic the human decision process. An expert system allows for easy encoding of expert knowledge as a set of rules. "Fuzziness" of the expert system allows better treatment of the uncertainties of the problem and to simplify the expert system itself. The fuzzy expert system has been designed based on a finite element (FE) model of a simple beam and has provided reliable detection of damage for every tested damage scenario (100% recognition). The same system recognized damages with 100% accuracy, and no false positives or negatives on mode shapes acquired by impact testing and/or non-contact laser vibrometer techniques.

Damage assessment of structures using modal test data

System health monitoring of structures is important not only for conducting safe operation but also maintaining system performance. In this paper, two identification algorithms for assessing structural damages using the modal test data have been developed. An important characteristic in the present approaches is that the employment of the global numerical models (e.g. FEM model) and some important information (e.g. Young’s modulus) of structures are avoided to a great extent. As the first step of the damage identification, two algorithms for the detection of damage location are proposed, which are similar in concept to the subspace rotation algorithm or best achievable eigenvector technique. Furthermore, a quadratic programming model is set up for the two approaches to predict the damage extent. To demonstrate the capability of the proposed approaches, an example of a 10-bay planar truss structure is employed for checking the present approaches numerically. Furthermore, the experimental data from the vibration test of a beam with two fixed ends are used directly in the present approaches. The final results show that the present techniques perform quite well in spite of the little structural information and measurement inaccuracies.

LOCALIZATION OF DAMAGED STRUCTURAL CONNECTIONS BASED ON EXPERIMENTAL MODAL AND SENSITIVITY ANALYSIS

A method for the detection of damage location for framed structures has been proposed. Unlike most of the existing methods, the proposed method has been proven by a series of experimental case studies and found to be practical in real applications. This paper reports the theoretical development of the proposed method, and the verification of the method with one numerical case study by using a six-storey frame and one experimental case study by using a two-storey frame. Two techniques have been developed in this proposed method: the Approximate Parameter Change (APC) technique and the Damage Signature Matching (DSM) technique. Both techniques employ measured modal data before and after the structure is damaged. The combination of these two techniques forms a practical method for the detection of damage locations.

Damage Detection of Buildings: Numerical and Experimental Studies

Identification of damage of structures has recently received considerable attention in the light of maintenance and retrofitting of existing structures under service loads and after natural disasters. In this respect, most of the previous system-identification studies considered structural models typically with not more than three degrees of freedom. Although many of these identification methods should work in principle, local damage detection of real structures remains an arduous task for reasons such as insensitivity of modal frequencies to local structural changes, oversimplification of the model, numerical difficulty in convergence, and inherent measurement noise. This paper presents an “improved condensation” method for identification of local damage of multistory frame buildings in terms of change in story stiffness. Static condensation is employed to reduce the system size. Identification is executed recursively on a remedial model, finally yielding integrity indices for all storys. By numerical simulation studies of a 12-story building with various noise levels, this method is shown to be feasible and computationally efficient. The efficacy of the method is further substantiated experimentally by damage detection of a 6-story laboratory model subjected to hammer tests.

Blue, Green and Red Fluorescence Signatures and Images of Tobacco Leaves*

AbstractLaser‐induced fluorescence images of the leaf of an aurea mutant of Nicotiana tabacum were recorded for the blue and green fluorescence at 440 and 520 nm and the red chlorophyll fluorescence at 690 and 735 nm. The results obtained were compared with direct measurements of the fluorescence emission spectra of leaves using a conventional spectrofluorometer. The highest emission of blue (F440) and green fluorescence (F520) within the leaf was found in the leaf veins, particularly the main leaf vein. In contrast, the intercostal fields of leaves, which exhibited the highest chlorophyll content, showed only a very low blue and green fluorescence emission, which was much lower than the red and far‐red chlorophyll fluorescence emission bands (F690 and F735). Correspondingly, the ratio of blue to red leaf fluorescence F440/F690 of upper and lower leaf side was much higher in the leaf veins (values 1.2 to 1.5) than in intercostal fields (values of 0.6 to 0.7). The results also demonstrated that in the intercostal fields the major part of the blue‐green fluorescence was reabsorbed by chlorophylls and carotenoids. A partial reabsorption of the red fluorescence band near 690 nm by leaf chlorophyll took place, but did not affect the far‐red fluorescence band near F735. As a consequence the chlorophyll fluorescence ratio F690/F735 exhibited significantly higher values in the chlorophyll‐poor leaf vein regions (1.7 to 1.8) than in the chlorophyll‐rich intercostal fields (0.8 to 1.3). Imaging spectroscopy of leaves was shown to be much more precise than the screening of fluorescence signatures by conventional fluorometers. It clearly demonstrated that the blue‐green fluorescence and the red chlorophyll fluorescence of leaves exhibit an inverse contrast to each other. The advantage of the fluorescence imaging spectroscopy, which allows the simultaneous screening of the whole leaf surface and distinct parts of it, and its possible application in the detection of stress effects or local damage by insects and pathogens, is discussed.