Papers by Keyword: SHM

Abstract: Structural health monitoring (SHM) is a modern technique for damage identification in the existing structure. The structural stiffness, frequency, damping, and dominant mode shapes represent the actual operating conditions of the structure. The main principle of structural health monitoring is to identify the modal parameters from experimental results both damaged and undamaged conditions. Damage is much effective to decrease stiffness and strength of structural components and it changes dynamic behaviour and damping ratio of whole structures. Bruel & Kjaer experimental modal analysis technique is recently used for civil engineering structures for modal parameters estimation. The paper describes the initial structural health monitoring of a steel frame. The modal parameters were estimated for undamaged conditions and these results are verified and updated by the numerical FEM tool SAP2000. For the undamaged structure, mode shapes and frequencies were calibrated properly. In the second step, damaged was initiated by dismantling one element from the lower part of the frame. The estimated modal parameters were compared to the initial one. The mode shapes and frequencies are quite different for some specific mode due to damage initiation. One extra mode was created for the damaged frame due to damage initiation. The 4^th mode was not found for the initial measurement because of presence of lower beam. Lower beam restraints the 4^th mode and the frame behaves more flexible. Keywords: SHM, Modal parameters, FEM modelling, Damage characterization, Experimental modal analysis (EMA).

Abstract: This paper presents a novel Convolutional Neural Network (CNN) based metamodel for impact detection and characterization for a Structural Health Monitoring (SHM) application. The signals recorded by PZT sensors during various impact events on a composite plate is used as inputs to CNN to detect and locate impact events. The input of the metamodel consists of 2D images, constructed from the signals recorded from a network of sensors. The developed meta-model was then developed and tested on a composite plate. The results show that the CNN-based metamodel is capable of detecting impacts with more than 98% accuracy. In addition, the network was capable of detecting impacts in the other regions of the panel, which was not trained with but had similar geometric configuration. The accuracy in this case was also above 98%, showing the scalability of this method for large complex structures of repeating zones such as composite stiffened panel.

Abstract: This work focuses on diagnostic methodologies for composite repair patch based on structural health monitoring (SHM) technology. Methodologies based on ultrasonic guided waves (GW) are developed and assessed for monitoring composite scarf repair with piezoelectric transducers. The effectiveness of the RAPID (reconstruction algorithm for probabilistic inspection of defects) algorithm was investigated for adhesively bonded composite patch repair. A composite scarf repair has been weakened by 4-point bending fatigue test and impacted after to generate a Barely Visible Damage (BVID). Both conventional RAPID technique, which requires baseline signals, and the Scaling Subtraction Method (SSM) were applied to detect damage in the bondline. The conventional method showed good performance for defect detection and localization whereas the SSM gives encouraging results for non-linear baseline-free RAPID.

Abstract: Conventional strain gauges made of constantan or CuCr for instance have a low value for structural health monitoring issues in plastic composites. These strain sensor materials exhibit small elastic regions and show fatigue when dynamically loaded with strain levels over 0.3 percent. For this reason, these sensors would break or fail before the composite life-time and thus cannot be integrated into this kind of composite materials. Pseudoelastic thermal shape memory alloys are therefore used as strain sensors and integrated into composites in order to allow piezoresistive strain measurement and structural health monitoring in such materials. Thermal treatments are used to create sensor structures out of shape memory alloy wires. Pseudoelastic shape memory wires can be strained up to 8 percent repeatedly. Their gauge factor is higher than 5. Shape memory strain sensors are successfully embedded into glass fibre reinforced plastics and show a significant and reproducible resistance change when the composite is strained. The dynamic strength is magnificently higher compared to conventional strain gauges. Shape memory strain sensors are an efficient alternative to fiber-bragg-grating sensors and can potentially be used for strain measurements in different plastics and textile materials. Shape memory sensor structures can be embedded or applied and are good candidates for structural characterisation and monitoring applications.

Abstract: The present article addresses the evaluation of the electro-mechanical (E/M) impedance method as a Structure Health Monitoring (SHM) method to detect and classify damage, more specific, the debonding of a face layer.In the study the considered structure is simplified as a circular sandwich panel of constant thickness, consisting of isotropic face layers and a honeycomb core.The debonding is assumed to be circular and situated at the center of the panel, only variable in its radius.The article starts with a brief introduction to the basic idea of SHM and the fundamentals of the E/M impedance method.Further, the idealized setting is investigated by two sets of experiments whose results are analyzed by typically used damage metrics and by considering both analytical and numerical models.A coupled-field FEM model is developed and compared to the experimental results.Furthermore, an analytical model is derived to evaluate the experimental and numerical results.All results are presented and discussed extensively on pursuing the objective to detect and classify the size of a debonding.Finally, it is shown how a model based approach can predict the presence but also the size of a debonding in the considered sandwich panels based on the E/M impedance measurements.

Abstract: Ultrasonic signal reconstruction for Structural Health Monitoring is a topic that has been discussed extensively. In this paper, we will apply the techniques of compressed sensing to reconstruct ultrasonic signals that are seriously damaged. To reconstruct the data, the application of conventional interpolation techniques is restricted under the criteria of Nyquist sampling theorem. The newly developed technique - compressed sensing breaks the limitations of Nyquist rate and provides effective results based upon sparse signal reconstruction. Sparse representation is constructed using Fourier transform basis. An l₁-norm optimization is then applied for reconstruction. Signals with temperature characteristics were synthetically created. We seriously corrupted these signals and tested the efficacy of our approach under two different scenarios. Firstly, the signal is randomly sampled at very low rates. Secondly, selected intervals were completely blank out. Simulation results show that the signals are effectively reconstructed. It outperforms conventional Spline interpolation in signal-to-noise ratio (SNR) with low variation, especially under very low data rates. This research demonstrates very promising results of using compressed sensing for ultrasonic signal reconstruction.

Abstract: Probability-based imaging which illustrates a distribution map of probability of damage presence in structures is a diagnostic method well established for damage detection in sensorized structures. Since the quality of the recorded signal is directly linked to the reliability of the diagnostic outcome, the assessment of robustness of the damage detection methodology is of high significance. In this paper, robustness and reliability of the current probability based imaging algorithms have been assessed for detecting BVID in a composite panel. Consequently, a proposed outlier analysis and DI probability distribution damage detection algorithm was shown to improve the reliability of the detection method.

Abstract: In this work the optimal configuration of transducers for damage detection and localization has been investigated. A particular interest is given to three optimization methods: mini-max, average Probability of Non Detection (POND) and ray tracing approach, coupled with genetic algorithm. After optimal configurations have been computed for each technique, they are experimentally tested and compared on a composite panel with one or two damages by generating and receiving Lamb waves signals. Damage detection is carried out with the Probability Based Damage Index Method (PBDIM). It was found that, in most cases, the ray tracing method and the average POND technique give better results, with a good detection of damages in comparison to the minimax POND technique, even if the latter seems numerically better.

Abstract: Bonded repair of composite structures still remains a crucial concern for the airworthiness authorities because of the uncertainty about the repair quality. This works, investigates the applicability of Structural Health Monitoring (SHM) techniques for monitoring of bonded repair. Active sensing method has been applied to two case studies: a sensorised panel impacted to cause barely visible impact damage (BVID) and repaired afterwards, the tensile and fatigue testing of a composite strap repair. In the first case, the previous sensors have been used to detect an artificially introduced damage. In the second case the failure of the adhesive during the tensile testing is used as basis of the load levels in the tensile-tensile fatigue test. In both cases PZT transducers have been used to monitor the bonded patch. An electromechanical impedance (EMI) and Lamb wave analysis have been carried out to check the overall integrity of the repair patch between. In both cases the state of the repaired composite was monitored successfully and reported.

Abstract: In this paper a novel concept of the MFC based IDT, Tunable Interdigital Transducer (T-IDT), is presented. The proposed transducer is the extension of the MFC based IDT, where the solid comb electrodes are replaced by series of discrete, stripe electrodes which can be connected independently into the groups and connected to the power source. The span between the centers of the electrodes' groups connected to the same phase are corresponding to the nominal wavelength of the wave excited by the transducer. This makes possible matching to different wavelengths without a need of physical changes of the electrodes’ layout.