Interaction of ultrasonic guided waves with interfacial debonding in a stiffened composite plate under variable temperature and operational conditions.

Stiffeners play a vital role in strengthening thin panels in a wide range of engineering constructions by reducing additional structural weight. However, these structures are vulnerable to issues such as interlayer delamination or skin-stiffener interfacial debonding due to high stress levels developed from external environmental conditions and operational loadings. In contrast, ultrasonic-guided wave (UGW) techniques exhibit an efficient and precise approach for monitoring discontinuities or damages in composite structures. There is a lack of research on understanding the characteristics of the interaction between UGW and interfacial debonding when in-plane edge loading and environmental factors are simultaneously taken into account. Therefore, this study is motivated by the need to develop a multiphysics numerical model which employs a commercially available finite element software, COMSOL Multiphysics®, to simulate UGW propagation in a stiffened composite plate with debonding at the plate-stiffener interface through a piezoelectric transducer under the combined influence of in-plane edge load and hygrothermal environment. The stiffened plate and piezoelectric patches are modelled with the tetrahedral element, and the bottom surface of the attached stiffener has a through-width 0.1 mm deep groove simulated for debonding. The developed FE model is validated against the results of the conducted experiments and those found in the available literature through the correlation coefficient. Further, the study conducts a comprehensive parametric investigation on stiffened cross-ply (0/90/0) laminated plates, considering variations in debonding size, in-plane load, and hygrothermal load intensity through the excitation of A0 mode. The acquired response is processed to compare the peak amplitude of various modes and energy of the waveform. Additionally, statistical indices such as normalised correlation moment (NCM) and variance of the continuous wavelet transform (CWT) peak are estimated to understand the impact of various parameters on waveform. The results show that the presence of a 90° lamina in the cross-ply laminate generates a low amplitude S0 mode in the scattered response. Moreover, a mode conversion from A0 to S0 mode is observed due to perfect bonding between the plate and the stiffener, providing insights into the bonding state in the panel. Furthermore, it is found that the magnitude of the in-plane loading marginally affects the peak amplitude of various modes in the scattered response. Additionally, when temperature intensity rises, the energy and amplitude of the UGW signals acquired through piezoelectric patches positioned in a direct line with the actuator gradually increase. The NCM value enhances with debonding regardless of exposed hygrothermal condition and reduces with increasing temperature intensity. In addition, the variance of the CWT peak reduces with debonding. The findings of this research are expected to be helpful for the development of efficient algorithms for detecting damages for structural health monitoring of stiffened composite panels.

Unsupervised deep learning framework for temperature-compensated damage assessment using ultrasonic guided waves on edge device.

Fueled by the rapid development of machine learning (ML) and greater access to cloud computing and graphics processing units, various deep learning based models have been proposed for improving performance of ultrasonic guided wave structural health monitoring (GW-SHM) systems, especially to counter complexity and heterogeneity in data due to varying environmental factors (e.g., temperature) and types of damages. Such models typically comprise of millions of trainable parameters, and therefore add to cost of deployment due to requirements of cloud connectivity and processing, thus limiting the scale of deployment of GW-SHM. In this work, we propose an alternative solution that leverages TinyML framework for development of light-weight ML models that could be directly deployed on embedded edge devices. The utility of our solution is illustrated by presenting an unsupervised learning framework for damage detection in honeycomb composite sandwich structure with disbond and delamination type of damages, validated using data generated by finite element simulations and experiments performed at various temperatures in the range 0-90 °C. We demonstrate a fully-integrated solution using a Xilinx Artix-7 FPGA for data acquisition and control, and edge-inference of damage. Despite the limited number of features, the lightweight model shows reasonably high accuracy, thereby enabling detection of small size defects with improved sensitivity on an edge device for online GW-SHM.

Unsupervised learning framework for temperature compensated damage identification and localization in ultrasonic guided wave SHM with transfer learning.

Damage localization algorithms for ultrasonic guided wave-based structural health monitoring (GW-SHM) typically utilize manually-defined features and supervised machine learning on data collected under various conditions. This scheme has limitations that affect prediction accuracy in practical settings when the model encounters data with a distribution different from that used for training, especially due to variation in environmental factors (e.g., temperature) and types of damages. While deep learning based models that overcome these limitations have been reported in literature, they typically comprise of millions of trainable parameters. As an alternative, we propose an unsupervised approach for temperature-compensated damage identification and localization in GW-SHM systems based on transferring learning from a convolutional auto encoder (TL-CAE). Remarkably, without using signals corresponding to the damages during training (unsupervised), our method demonstrates more accurate damage detection and localization as well as robustness to temperature variations than supervised approaches reported on the publicly available Open Guided Waves (OGW) dataset. Additionally, we have demonstrated reduction in number of trainable parameters using transfer learning (TL) to leverage similarities between time-series among various sensor paths. It is also worth noting that the proposed framework uses raw time-domain signals, without any pre-processing or knowledge of material properties. It should therefore be scalable and adaptable for other materials, structures, damages, and temperature ranges, should more data become available in the future. We present an extensive parametric study to demonstrate feasibility of the proposed method.

Quantitative Investigation of Acoustic Emission Waveform Parameters from Crack Opening in a Rail Section Using Clustering Algorithms and Advanced Signal Processing.

Acoustic emission (AE) is an emerging technology for real-time non-destructive testing of structures. While research on a simulated AE source in rail and testing on rail material using small beam samples have been conducted, a study is required in lab environment to investigate AE waveform characteristics generated by crack in rail. In this paper, a three-point bending test is conducted on an actual rail section of 1500 mm with transverse damage of 38% head area to simulate AE source due to crack opening in the rail. AE signals are recorded for three different loads. For data analysis, unsupervised machine learning algorithms such as k-means, fuzzy-C mean and gaussian mixture model are used to cluster and filter out usable signals from the whole dataset corrupted by noisy signals from various sources. k-mean with principal component was observed to be best technique based on silhouette score. The frequency and amplitude of waveform have been discussed in relation to load and crack opening displacement. This study establishes a baseline for linking load, crack opening, and AE wave characteristics. This work can ultimately aid in the development of robust denoising, and damage analysis algorithms based on the frequency content and dispersion of the AE waveform.

Performance evaluation of compressive sensing based lost data recovery using OMP for damage index estimation in ultrasonic SHM.

Compressive sensing (CS) has been widely explored for data compression and signal recovery in presence of lossy transmission in structural health monitoring (SHM) applications. Discussions of lost data recovery using CS reported in literature are typically limited to acceleration signals obtained from vibration based SHM systems. Moreover these reports limit the study to performance analysis of recovery of signals in time domain, while feasibility of these algorithm on subsequent damage analysis using recovered signals remains unexplored. A systematic evaluation of performance of CS based signal recovery for algorithmic estimation of damage index (DI) in ultrasound SHM systems is important for determining their practicality for automated SHM applications. In this paper, we study the feasibility of DI estimation in ultrasonic guided wave testing of honeycomb composite sandwich structures (HCSS) using signals recovered from lossy sensor recordings. We emulate signal loss by masking the sensor recordings in an experimentally measured dataset comprising of an HCSS panel with two defects (disbond and high density (HD) core) instrumented with eight piezoelectric wafer and employ orthogonal matching pursuit (OMP) based signal recovery algorithm. Our analysis suggests that while OMP-based signal recovery algorithm is a robust and reliable signal recovery technique, producing signal reconstruction errors lesser than 8.4% for data loss as high as 50%, the magnitude error in DI estimation is significant and varies for different signal difference coefficient (SDC) algorithms. We propose alternate SDC definition, SDCPA, computed using peak amplitude of the Hilbert transform (HT), that shows consistently less error than the conventional cumulative-sum-based SDC definition for the HCSS case study. Further we study trends of error in recovery of lossy time domain signals as well as DI computation as a function of data loss parameters, for both random as well as continuous data loss. Our findings indicate that conventional DI computation algorithms for ultrasonic SHM need to be revisited when used in compressive sensing paradigm.

Online acoustic emission source localization in concrete structures using iterative and evolutionary algorithms.

This study is motivated by the need to develop an efficient online structural health monitoring (SHM) framework to accurately localize damage induced acoustic emission (AE) sources in concrete structures. Experimental studies are carried out in concrete slabs considering pencil lead break (PLB) as artificial damage source to initialize acoustic emission (AE) waves. A simplified yet robust Iterative Planar Source (IPS) localization algorithm based on arrival time (ToA) is proposed first to identify arbitrarily selected several damage source locations for (1) rectangular (2)circular, and (3) zig-zag distributed AE sensor network arrangements. The results of the proposed localization algorithm are then compared with those obtained from an evolutionary particle-swarm optimization (PSO) algorithm for each sensor network arrangement to assess the performance of the AE source monitoring strategy. It is found that the zig-zag arrangement of the distributed AE sensors is the most efficient sensor network arrangement for damage source localization. The obtained results clearly represent the accuracy and robustness of the proposed online monitoring framework for localizing damage induced acoustic emission sources in concrete structures without extensive manual intervention.

Guided wave propagation in a honeycomb composite sandwich structure in presence of a high density core.

A coordinated theoretical, numerical and experimental study is carried out in an effort to interpret the characteristics of propagating guided Lamb wave modes in presence of a high-density (HD) core region in a honeycomb composite sandwich structure (HCSS). Initially, a two-dimensional (2D) semi-analytical model based on the global matrix method is used to study the response and dispersion characteristics of the HCSS with a soft core. Due to the complex structural characteristics, the study of guided wave (GW) propagation in HCSS with HD-core region inherently poses many challenges. Therefore, a numerical simulation of GW propagation in the HCSS with and without the HD-core region is carried out, using surface-bonded piezoelectric wafer transducer (PWT) network. From the numerical results, it is observed that the presence of HD-core significantly decreases both the group velocity and the amplitude of the received GW signal. Laboratory experiments are then conducted in order to verify the theoretical and numerical results. A good agreement between the theoretical, numerical and experimental results is observed in all the cases studied. An extensive parametric study is also carried out for a range of HD-core sizes and densities in order to study the effect due to the change in size and density of the HD zone on the characteristics of propagating GW modes. It is found that the amplitudes and group velocities of the GW modes decrease with the increase in HD-core width and density.

An automated damage identification technique based on vibration and wave propagation data.

This paper is concerned with the detection and characterization of hidden defects in advanced structures before they grow to a critical size. A novel method is developed using a combination of vibration and wave propagation data to determine the location and degree of damage in structural components requiring minimal operator intervention. The structural component is to be instrumented with an array of actuators and sensors to excite and record its dynamic response. A damage index, calculated from the measured dynamic response of the structure in a reference state (baseline) and the current state, is introduced as a determinant of structural damage. The index is a relative measure comparing the two states of the structure under the same ambient conditions. The indices are used to identify damages in the forms of delaminations and holes in composite plates for different arrangements of the source and the receivers. The potential applications of the approach in developing health monitoring systems in defects-critical structures are discussed.