Search

The present research proposes a two-step physics- and machine-learning(ML)-based electromechanical impedance (EMI) measurement data evaluation approach for sandwich face layer debonding detection and size estimation in structural health monitoring (SHM) applications. As a case example, a circular aluminum sandwich panel with idealized face layer debonding was used. Both the sensor and debonding were located at the center of the sandwich. Synthetic EMI spectra were generated by a finite-element(FE)-based parameter study, and were used for feature engineering and ML model training and development. Calibration of the real-world EMI measurement data was shown to overcome the FE model simplifications, enabling their evaluation by the found synthetic data-based features and models. The data preprocessing and ML models were validated by unseen real-world EMI measurement data collected in a laboratory environment. The best detection and size estimation performances were found for a One-Class Support Vector Machine and a K-Nearest Neighbor model, respectively, which clearly showed reliable identification of relevant debonding sizes. Furthermore, the approach was shown to be robust against unknown artificial disturbances, and outperformed a previous method for debonding size estimation. The data and code used in this study are provided in their entirety, to enhance comprehensibility, and to encourage future research. [ABSTRACT FROM AUTHOR]

In recent years, structural health monitoring has been increasingly applied to composite sandwich structures, as typically used in aerospace applications. In addition, machine learning approaches are increasingly popular for damage detection, localization and size estimation, due to their great advantages in pattern recognition and anomaly detection. However, a major disadvantage of machine learning techniques is that these algorithms generally require large amounts of realistic data. In general, these data are expensive or even impossible to obtain within a feasible time. In order to overcome this hindrance, this work introduces a computationally inexpensive framework for physics-driven feature generation of strain data for the training of ML-based SHM methods using sub-structuring and the concept of reanalysis. First, the global FE model is subdivided into a monitored part, i.e., a smaller submodel, and a global model. Second, the stiffness matrix of the submodel is extracted from the finite element software. Then, static condensation is performed to further reduce the computational effort. Afterwards, selected eigenvectors are derived in terms of displacements of master nodes and the corresponding strains are calculated. Finally, a statistically varied linear combination between the different characteristic eigenvector load cases is performed based on the superposition principle. This procedure enables the efficient generation of a large number of different physics-driven determined strain solutions for a subsequent training of a ML algorithms. The proposed framework is evaluated by means of a damage detection approach, based on an artificial neuronal network classifier algorithm. The applied approach utilizes strain measurements from selected positions as physical quantity and is demonstrated using a composite sandwich structure imitating an aircraft spoiler. The key principle of the damage detection algorithm is based on the fact that a change in the relationship between sensors indicates the presence of damage. Additionally, to the numerical healthy strains resulting from the framework, synthetically generated damage data are used for training the neuronal network classifier. The synthetic data are obtained by statistical modifications of the healthy strains, to avoid time-consuming and expensive damage simulations. The feature generation framework and health monitoring approach are validated using experiments and numerical simulations of a glass fiber reinforced polymer sandwich structure with a hole considered as damage. The presented numerical and the experimental results clearly show the high potential for the efficient approach for damage detection in a sandwich structure. [ABSTRACT FROM AUTHOR]

The present paper comprehensively investigates the complex interaction between ultrasonic guided waves and local structural discontinuities, such as damages, through highly sensitive features: so-called wave damage interaction coefficients (WDICs). These WDICs are unique for each structural discontinuity and depend solely on their characteristics for a given structure and condition. Thus, they can be particularly useful for advanced assessment of lightweight structures in the context of non-destructive evaluation and structural health monitoring. However, the practical application of WDICs entails significant difficulties due to their sensitivity and complex patterns. Therefore, this study attempts to guide researchers and practitioners in the estimation of WDICs from numerical simulations and physical experiments. Detailed investigations are made for an aluminum host plate modified by artificial structural discontinuities, i.e., surface-bonded steel sheets. The numerical simulations are performed to predict WDICs and study sensitivities using a sophisticated finite element model. The experimental setup uses piezoelectric transducers to excite guided waves in the host plate. A single scanning laser Doppler vibrometer measures the scattered guided waves caused by the surface-bonded steel sheets, and the resulting WDICs with possible influences are investigated. In both cases, the orientation and thickness of the attached steel sheets were varied to create 12 different damage scenarios. In general, the comparison between numerical and experimental WDICs show good agreement. This underpins the applicability of the general methodology for simulating and measuring WDICs over all scenarios. Furthermore, the WDIC scattering patterns reveal a clear dependency of the peaks in the back-scattered reflections for both the numerical and experimental amplitude coefficients on the damage orientation, basically following the law of reflection. However, some discrepancies between both studies were observed. Numerical sensitivity analysis identified the adhesive layer as one reason for such differences. Additionally, misalignment errors in the experimental measurements were also found to affect WDICs. Therefore, an improved baseline subtraction method is proposed, which clearly enhances the experimental WDICs. Finally, an experimental sensitivity study of WDICs for selected sensing radii revealed only a minor influence. All these investigations were made for the amplitude as well as the phase representation of WDICs. Thus, these findings may open the way to future research and development of techniques employing WDICs for advanced applications of non-destructive evaluation and structural health monitoring. [ABSTRACT FROM AUTHOR]

The present research addresses structural health monitoring of pinned, composite–titanium (i.e.: hybrid) joints with the aim of using their lightweight potential and damage tolerance in future aircraft designs. Together with additively manufactured titanium pins, protruding into the carbon-fiber composite, a single-lap shear joint specimen is monitored with direct current electrical resistance measurements (DC ERM) across the overlap, without conductivity-enhancing additives (e.g., carbon nanotubes), but rather with the pins' complex electrical network that forms with the carbon-fiber composite. For a proof-of-concept demonstration, a structural test with quasi-static, tension–tension loading and unloading is performed. Using digital image correlation, degradation of the joint is monitored. Results are validated by a 2-dimensional finite element model, considering multiple damage states. For DC ERM, a damage indicator is proposed to evaluate the joint's structural condition. It is shown that typical damage for this joint type reported literature (i.e., cracks occurring at the overlap ends) could be reproduced and detected by the electrical property change across the overlap. Under the given laboratory conditions, the proposed DC ERM damage indicator clearly shows a non-reversible increase in resistance by 3.8% due to damage, starting at first damage initiation and also reflecting further damage growth. Thereby, the method's capability for damage detection and monitoring is demonstrated. [ABSTRACT FROM AUTHOR]

This paper investigates the potential of a novel vibration-based thermal health monitoring method for continuous and on-board damage detection in fiber reinforced polymer sandwich structures, as typically used in aerospace applications. This novel structural health monitoring method uses the same principles, which are used for vibration-based thermography in combination with the concept of the local defect resonance, as a well known non-destructive testing method (NDT). The use of heavy shakers for applying strong excitation and infrared cameras for observing thermal responses are key hindrances for the application of vibration-based thermography in real-life structures. However, the present study circumvents these limitations by using piezoelectric wafer active sensors as excitation source, which can be permanently bonded on mechanical structures. Additionally, infrared cameras are replaced by surface temperature sensors for observing the thermal responses due to vibrations and damage. This makes continuous and on-board thermal health monitoring possible. The new method is experimentally validated in laboratory experiments by a sandwich structure with face layer debonding as damage scenario. The debonding is realized by introduction of an insert during the manufacturing process of the specimen. The surface temperature sensor results successfully show the temperature increase in the area of the debonding caused by a sinusoidal excitation of the sandwich structure with the PWAS at the first resonance frequency of the damage. This is validated by conventional infrared thermography. These findings demonstrate the potential of the proposed novel thermal health monitoring method for detecting, localizing and estimating sizes of face layer debonding in sandwich structures. [ABSTRACT FROM AUTHOR]

An idealized 1:2 scale demonstrator and a numerical parameter optimization algorithm are proposed to closely reproduce the deformation shape and, thus, spatial strain directions of a real aerodynamically loaded civil aircraft spoiler using only four concentrated loads. Cost-efficient experimental studies on demonstrators of increasing complexity are required to transfer knowledge from coupons to full-scale structures and to build up confidence in novel structural health monitoring (SHM) technologies. Especially for testing novel sensor systems that depend on or are affected by mechanical strains, e.g., strain-based SHM methods, it is essential that the considered lab-scale structures reflect the strain states of the real structure at operational loading conditions. Finite element simulations with detailed models were performed for static strength analysis and for comparison to experimental measurements. The simulated and measured deformations and spatial strain directions of the idealized demonstrator correlated well with the numerical results of the real aircraft spoiler. Thus, using the developed idealized demonstrator, strain-based SHM systems can be tested under conditions that reflect operational aerodynamic pressure loads, while the test effort and costs are significantly reduced. Furthermore, the presented loading optimization algorithm can be easily adapted to mimic other pressure loads in plate-like structures to reproduce specific structural conditions. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) is the continuous on-board monitoring of a structure's condition during operation by integrated systems of sensors. SHM is believed to have the potential to increase the safety of the structure while reducing its deadweight and downtime. Numerous SHM methods exist that allow the observation and assessment of different damages of different kinds of structures. Recently data fusion on different levels has been getting attention for joint damage evaluation by different SHM methods to achieve increased assessment accuracy and reliability. However, little attention is given to the question of which SHM methods are promising to combine. The current article addresses this issue by demonstrating the theoretical capabilities of a number of prominent SHM methods by comparing their fundamental physical models to the actual effects of damage on metal and composite structures. Furthermore, an overview of the state-of-the-art damage assessment concepts for different levels of SHM is given. As a result, dynamic SHM methods using ultrasonic waves and vibrations appear to be very powerful but suffer from their sensitivity to environmental influences. Combining such dynamic methods with static strain-based or conductivity-based methods and with additional sensors for environmental entities might yield a robust multi-sensor SHM approach. For demonstration, a potent system of sensors is defined and a possible joint data evaluation scheme for a multi-sensor SHM approach is presented. [ABSTRACT FROM AUTHOR]

To ensure the structural integrity of large aerospace structures during operation, structural health monitoring is a major challenge. The monitoring can be performed by distributed strain measurements using strain gauges or fiber optical sensors. In this work, an advanced local residual classifier for strain-based damage detection and localization is introduced. The key principle is that a change in the relationship between a strain sensor and its neighbors indicates the presence of damage. After defining a sensor grid with sensor locations and their orientation, the relationship can be obtained from numerical simulations of the healthy structure. Here, local regression models are estimated between each master sensor and its neighboring sensors. Then, residuals of the predicted and measured strains are evaluated using a random forest classifier. The evaluation of the residuals has the advantage that the method is independent of the load level, as well as the fact that it is independent of certain environmental influences that are uniformly distributed over the entire structure. In addition to the numerical healthy strains, synthetically generated damage data are used for training the classifier. The synthetic data are obtained by statistical modifications of the healthy strains. This procedure avoids time-consuming and expensive damage simulations. The health monitoring approach is applied to a glass fiber reinforced polymer sandwich structure, imitating an aircraft spoiler, with a hole in the face layer considered as damage. The validation is performed by numerical finite element simulations as well as physical experiments under random loading conditions. The results demonstrate the high potential of the presented approach for strain-based structural health monitoring in composite sandwich structures. [ABSTRACT FROM AUTHOR]

The ever-increasing demand for efficiency and cost improvements in lightweight structures with guaranteed safety and reliability is leading to the application of a damage-tolerant design philosophy. Here, accurate knowledge of structural health is critical to avoid catastrophic failures. This knowledge can be obtained by using advanced structural health monitoring (SHM) systems. For thin-walled lightweight structures, methods utilizing guided waves generated by piezoelectric transducers are well suited. The interaction between the guided waves and potential damages can be described by so-called wave damage interaction coefficients (WDICs). These WDICs are unique for each damage and depend solely on its characteristics for a given structure. Therefore, the comparison of known WDICs with estimated ones allows drawing conclusions about the current structural state. In this paper, a novel damage identification method for plate-like structures based on a database of such WDICs is presented. Selected damages are simulated numerically with finite elements to generate WDIC patterns. However, these simulations are computationally highly demanding, thus only a very limited number of damage scenarios can be simulated. This study proposes an innovative technique to substantially enhance the resulting WDIC database by using deep neural networks (DNNs). These DNNs enable smart interpolations and allow not only predicting WDICs for previously unseen damages at low computational costs but also the discovery of knowledge about the complex relationship between damage features and WDIC patterns. A comparison to other machine learning algorithms clearly shows the superior performance of the utilized DNNs for interpolating complex WDIC patterns. The proposed damage identification method is verified using advanced time-domain simulations of a large aluminum plate. A statistical analysis of correct identification rates in a common three-sensor setting is employed for assessing the general performance. It is demonstrated that carefully identified DNNs enable to accurately replicate and interpolate complex WDIC patterns. Furthermore, it is shown that these predicted WDICs allow identifying damage characteristics with high confidence. • We present a novel technique for predicting WDICs by means of DNNs • We propose a collective representation of WDICs to enable the discovery of knowledge • We show complex relationships between damage features and WDIC patterns • We demonstrate damage identification using a WDIC database extended by DNNs [ABSTRACT FROM AUTHOR]

• A SHM method is presented and discussed on the example of an aircraft spoiler. • An in-depth numerical study with varying damage sizes is performed. • The validation of the numerical results is done by a DIC system and SG measurements. This paper investigates a strain-based structural health monitoring (SHM) method for damage detection and localization in composite sandwich structures. As case example, an idealized aircraft spoiler of a large civil aircraft is considered. Critical failure modes of such sandwich structures composed of fiber reinforced polymer (FRP) face layers and a honeycomb core are, e.g., face layer delamination and debonding from the core. The latter challenges today's non-destructive testing methods, and thus, shall be addressed in the present work. The presented research compromises an idealized spoiler model of the real spoiler structure in the scale 1:2. The model is composed of glass fiber reinforce polymer (GFRP) face layers and a honeycomb core. The damage identification is investigated based on static strain measurements along so-called zero-strain trajectories (ZST) at the loaded structure. A zero-strain direction exists for every plane strain state with major strain directions with opposite signs (tensile and compression). Connecting zero-strain direction vectors at various points of a structure yields a ZST. Strains along ZST are most sensitive to changes of the load path due to, e.g., damage. This work investigates the use of strain measurements along ZST for damage detection at the edge of the considered sandwich structure by means of numerical and experimental analysis. A real wind-load condition of a large civil aircraft spoiler is considered as load case. An in-depth numerical and experimental investigation is performed to calculate the ZST for the pristine structure. The experimental validation of the Finite element (FE) model is realized by deformation measurements via a digital image correlation system (DIC). Moreover, strain measurements via strain gauge (SG) rosettes were performed to evaluate the correct calculation of the zero-strain directions. Finally, the application of strain measurements along a ZST for debonding detection and localization is demonstrated and its potential and issues for real online monitoring is discussed. [ABSTRACT FROM AUTHOR]