Deep learning for automatic assessment of breathing-debonds in stiffened composite panels using non-linear guided wave signals

This paper presents a new structural health monitoring strategy based on a deep learning architecture that uses nonlinear ultrasonic signals for the automatic assessment of breathing-like debonds in lightweight stiffened composite panels (SCPs). Towards this, nonlinear finite element simulations of ultrasonic guided wave (GW) response of SCPs and laboratory-based experiments have been undertaken on multiple composite panels with and without baseplate-stiffener debonds using fixed a network of piezoelectric transducers (actuators/sensors). GW signals in the time domain are collected from the network of sensors onboard the SCPs and these signals in the frequency domain represent nonlinear signatures as the existence of higher harmonics. These higher harmonic signals are separated from the GWs (raw) and converted to images of time-frequency scalograms using continuous wavelet transforms. A deep learning architecture is designed that uses the convolutional neural network to automatically extract the discrete image features for the characterization of SCP under healthy and variable breathing-debond conditions. The proposed deep learning-aided health monitoring strategy demonstrates a promising autonomous inspection potential with high accuracy for such complex structures subjected to multi-level breathing-debond regions

A Combined Machine Learning and Model Updating Method for Autonomous Monitoring of Bolted Connections in Steel Frame Structures Using Vibration Data

This research paper presents a novel structural health monitoring strategy based on a hybrid machine learning and finite element model updating method for the health monitoring of bolted connections in steel planer frame structures using vibration data. Towards this, a support vector machine model is trained with the discriminative features obtained from time history data, and those features are used to distinguish between damaged and undamaged joints. An FE model of the planer frame is considered where the fixity factor (FF) of a joint is modeled with rational springs and the FF of the spring is assumed as the severity level of loosening bolts. The Cat Swarm Optimization technique is further applied to update the FE model to calculate the fixity factors of damaged joints. Initially, the method is applied to a laboratory-based experimental model of a single-story planer frame structure and later extended to a pseudo-numerical four-story planer frame structure. The results show that the method successfully localizes the damaged joints and estimates their fixity factors

An optimized data fusion strategy for structural damage assessment using electromechanical impedance

This paper proposes a new sensor network optimized data fusion approach for structural health monitoring of metallic structures using electromechanical impedance (EMI) signals. The integrated approach used to fuse common healthy state baseline model based damage detection, quantification and classification in EMI technique. Towards this, the principal component analysis (PCA) is carried out and corresponding the root mean square deviation (RMSD) index is calculated to study the information of piezoelectric transducer’s impedance (|Z|), admittance (|Y|), resistance (R), and conductance (G) in the frequency domain. A new optimized data fusion approach is proposed which was realized at the sensor level using the PCA as well as at the variable level using self-organizing maps (SOMs). The SOM comparative studies are performed using the Q-statistics (Q index) and the Hotelling’s T2 statistic (T index). The proposed methodology is tested and validated for an aluminum plate with multiple drilled holes with variable size and locations. In the process, a centralized data-fused baseline eigenvector is prepared from a healthy structure and the damage responses are projected on this baseline model. The statistical, data-driven damage matrices are calculated and compared with the RMSD index and used in a fusion based data classification using SOM. The proposed method shows robust damage sensitivity for hole locations and hole enlargement irrespective of the wide frequency range selection, and the selected frequency range contains the resonant frequency range

Multi step structural health monitoring approaches in debonding assessment in a sandwich honeycomb composite structure using ultrasonic guided waves

This paper aims to investigate the use of ultrasonic guided wave (GW) propagation mechanism and the assessment of debonding in a sandwich composite structure (SCS) using a multi-step approach. Towards this, a series of GW propagation-based laboratory experiments and numerical simulations have been carried out on the SCS sample. The debonding regions of variable size and locations were assessed using a pre-defined network of piezoelectric lead zirconate transducers (PZT). Besides, several artificial masses were also placed in the SCS to validate the multi-step structural health monitoring (SHM) strategy. The SHM approach uses a proposed quick damage identification matrix maps and an improved elliptical wave processing (EWP) strategy of the registered GW signals to detect the locations of debonding and other damages in the SCS. The benefit of the proposed damage identification map is to locate the damaged area (sectors) quickly. This identification step is followed by applying the damage localization step using the improved EWP only on the previously identified damage sector region. The proposed EWP has shown the potential to effectively locate the hidden multiple debonding regions and damages in the SCS with a reduced number of calculations using a step-wise approach that uses only a selected number of grid points. The paper shows the effectiveness of the proposed approach based on data gathered from numerical simulations and experimental studies. Thus, using the above-mentioned SHM strategy debondings and damages present within and outside the sensor network are localized. The results were cross verified with nondestructive testing (NDT) methods such as infrared thermography and laser Doppler vibrometry

A global-local damage localization and quantification approach in composite structures using ultrasonic guided waves and active infrared thermography

The paper emphasizes an effective quantification of hidden damage in composite structures using ultrasonic guided wave (GW) propagation-based structural health monitoring (SHM) and an artificial neural network (ANN) based active infrared thermography (IRT) analysis. In recent years, there has been increased interest in using a global-local approach for damage localization purposes. The global approach is mainly used in identifying the damage, while the local approach is quantifying. This paper presents a proof-of-study to use such a global-local approach in damage localization and quantification. The main novelties in this paper are the implementation of an improved SHM GW algorithm to localize the damages, a new pixel-based confusion matrix to quantify the size of the damage threshold, and a newly developed IRT-ANN algorithm to validate the damage quantification. From the SHM methodology, it is realized that only three sensors are sufficient to localize the damage, and an ANN- IRT imaging algorithm with only five hidden neurons in quantifying the damage. The robust SHM methods effectively identified, localized, and quantified the different damage dimensions against the non-destructive testing-IRT method in different composite structures

Nondestructive analysis of debonds in a composite structure under variable temperature conditions

This paper presents a nondestructive analysis of debonds in an adhesively-bonded carbon-fibre reinforced composite structure under variable temperature conditions. Towards this, ultrasonic guided wave propagation based experimental analysis and numerical simulations are carried out for a sample composite structure to investigate the wave propagation characteristics and detect debonds under variable operating temperature conditions. The analysis revealed that the presence of debonds in the structure significantly reduces the wave mode amplitudes, and this effect further increases with the increase in ambient temperature and debond size. Based on the debond induced differential amplitude phenomenon, an online monitoring strategy is proposed that directly uses the guided wave signals from the distributed piezoelectric sensor network to localize the hidden debonds in the structure. Debond index maps generated from the proposed monitoring strategy show the debond identification potential in the adhesively-bonded composite structure. The accuracy of the monitoring strategy is successfully verified with non-contact active infrared-thermography analysis results. The effectiveness of the proposed monitoring strategy is further investigated for the variable debond size and ambient temperature conditions. The study establishes the potential for using the proposed damage index constructed from the differential guided wave signal features as a basis for localization and characterization of debond damages in operational composite structures

Multi-level nondestructive analysis of joint-debond effects in sandwich composite structure

This paper presents a non-destructive analysis of joint-debond effects on propagating guided wave modes under variable ambient temperature conditions and proposal of an online monitoring strategy for identification of single as well as multiple joint-debond regions in a sandwich composite structure (SCS). A semi-analytical analysis of guided wave dispersion in a SCS was carried out to identify different wave modes within the operating frequency range. An extensive analysis of core-core joint-debond effects on the guided wave modes under variable temperature conditions was studied by performing experimental investigations and validated finite element simulations. The analysis results showed that the presence of joint-debond at the core-core interface significantly increases the wave mode amplitudes and the increase in ambient temperature further increases the amplitude difference between the bonded and debonded-influenced signals. An online monitoring strategy is proposed to effectively identify the hidden joint-debonds in SCS based on the extracted differential features of debond effects in the wave mode amplitudes of the sensor signals

Autonomous Monitoring of Breathing Debonds in Bonded Composite Structures Using Nonlinear Ultrasonic Signals

This study aims to develop a structural health monitoring model that autonomously assesses breathing-type debonds between the base plate and stiffener in lightweight composite structures. The approach utilizes a specifically designed deep learning architecture that employs nonlinear ultrasonic signals for automatic debond assessment. To achieve this, a series of laboratory experiments were conducted on multiple composite panels with and without base plate-stiffener debonds. A network of piezoelectric transducers (actuators/sensors) was used to collect time-domain guided wave signals from the composite structures. These signals, representing nonlinear signatures such as higher harmonics, were separated from the raw signals and transformed into time-frequency scalograms using continuous wavelet transforms. A convolutional neural network-based deep learning architecture was designed to extract discrete image features automatically, enabling the characterization of composite structures under healthy and variable breathing-debond conditions. The proposed deep learning-assisted health monitoring model exhibits promising potential for autonomous inspection with high accuracy in complex structures that experience breathing-debonds.</p

Study of guided wave propagation in a honeycomb composite sandwich plate in presence of a high-density core region using surface-bonded piezoelectric transducers

"Honeycomb Composite Sandwich Structure" (HCSS), is a novel material that has been adopted globally as a major structural component in aerospace, marine and automotive vehicles due to its high strength to weight ratio and high energy-absorption capability. In this study, a combined numerical and experimental study is carried out in an effort to understand the attributes of the propagating Guided Wave (GW) modes in the presence of a High-Density (HD) core zone in a HCSS. Owing to the complex structural characteristics, the GW propagation study in HCSS with HD-core zone inherently possesses many challenges. Therefore, Two Dimensional (2D) numerical simulations of wave propagation in the HCSS without and with HD-core region are accomplished using surface bonded Piezoelectric Wafer Transducers (PWTs). Results of the numerical study show that the presence of the HD core leads to substantial decrease in the amplitude and the group velocity of the output GW signal. In order to validate the results of the simulation, experiments were conducted, which shows good agreement between the experimental and numerical results is in all the cases considered. In order to study the effect of size of the HD core zone on the group velocity and the amplitude of the propagating wave modes, a parametric study is also carried out for a selected range of the HD core widths. It is observed that the group velocity and the amplitude of the received GW modes are just about inversely proportional to the HD core width

Guided wave based nondestructive analysis of localized inhomogeneity effects in an advanced sandwich composite structure

In this paper, we present a nondestructive analysis of localized inhomogeneity effects on guided wave propagation in an advanced sandwich composite structure. In the process, guided wave dispersion curves were semi-analytically determined for the structure to accurately identify different wave modes in experimental and numerical analysis signals. Finite element simulation of wave propagation in the target structure was then carried out in ABAQUS and validated with the experiment. Significant influences on the wave mode amplitudes were observed due to the presence of a localized inhomogeneity in the structure. An inhomogeneity identification strategy was prepared based on the amplitude changes in the registered guided wave signals from a predefined piezoelectric transducer network. The influence of varying elastic modulus and mass-density of the inhomogeneous region on the wave mode amplitudes and the corresponding inhomogeneity-index magnitudes were also studied