Reconstruction Algorithm For Probabilistic Inspection Research Articles

Research on Delamination Damage Localization of Carbon Fiber-Reinforced Polymer Curved Plate Using Lamb Wave

Carbon fiber-reinforced polymers (CFRPs) are extensively employed in the aerospace industry due to their excellent properties. Delamination damage occurring at critical locations in CFRPs can seriously reduce the safety of in-service components. The detection and localization of delamination damage using Lamb waves hold significant potential for widespread application in non-destructive testing. However, the choice of damage localization algorithm may produce different delamination damage localization results. This research presented an IRAPID (improved reconstruction algorithm for probabilistic inspection of defects) method derived from the RAPID (reconstruction algorithm for probabilistic inspection of defects) method, aiming to improve the accuracy and reliability of delamination damage localization. Three CFRP curved plates, including a healthy curved plate and two curved plates with delamination damage sizes of Φ20 mm and Φ40 mm, were prepared in the experiment. The detection experiment of the CFRP curved plate using lead zirconate titanate (PZT) as a transducer to excite and receive Lamb waves was conducted, and the influence of excitation signal frequency on the performance of the proposed method was discussed. Under the condition of an excitation signal frequency of 220~320 kHz and a step size of 10 kHz, the accuracy of the delamination damage localization method proposed in this paper was compared with that of existing methods. The experimental results indicate that the IRAPID algorithm exhibits good stability in the localization of delamination damage across the range of frequency variations considered. The localization error of the IRAPID algorithm for delamination damage is significantly lower than that of the DaS (delay-and-sum) algorithm and the RAPID algorithm. As the size of the delamination damage increases, so does the localization error. The accuracy of delamination damage localization is lower in the X-axis direction than in the Y-axis direction. By averaging the localization results across various frequencies, we can mitigate the potential localization errors associated with single-frequency detection to a certain extent. For the localization of delamination damage, Lamb waves at multiple frequencies can be employed for detection, and the detection results at each frequency are averaged to enhance the reliability of localization.

Research on spatial localization method of composite damage under strong noise

In this paper, a damage spatial imaging approach based on novel signal extraction is suggested to reconstruct the Lamb wave response signal under strong noise and realize the spatial localization of damage. First, the Variable Mode Decomposition (VMD) parameters are optimized by the improved Grey Wolf optimization method (IGWO) to decompose the response signal. To rebuild the response signal, the correlation coefficient is used to choose the optimal modal component and the residual. To give the best wavelet function and transform level for adaptive denoising of the reconstructed signal without a reference signal, an enhanced Discrete Wavelet Transform (DWT) based on Shannon entropy is proposed. To achieve damage localization imaging, a damage spatial localization model is built utilizing a reconstruction algorithm for probabilistic inspection of damage (RAPID) approach and a convolutional neural network (CNN). The suggested method may successfully increase the signal-to-noise ratio (SNR) of the reconstructed response signal and lower the error of spatial localization under strong noise through experiments. The spatial localization of composite damage using Lamb wave under strong noise is expanded in this paper.

Research on multiple damage localisation based on fusion of the Lamb wave ellipse algorithm and RAPID algorithm

Current damage localisation methods often require many sensors and complex signal processing methods. This paper proposes a fusion algorithm based on elliptical localisation and the reconstruction algorithm for probabilistic inspection of damage (RAPID) to locate and image multiple damages. Experimental verification of the damage algorithm was conducted. An ultrasonic probe was used to excite Lamb signals on an aluminium alloy plate, the ultrasonic response signals at different positions within the plate under multiple damages were measured and the constructed algorithm was employed to image the damage location. In the experiment, this method improved localisation efficiency by excluding invalid sensing paths in the sensor network, saving 31.32% of computational time. When some sensors in the sensor network were damaged, this algorithm ensured a positioning accuracy with a positioning error of 5.83 mm. Finally, the algorithm was used to locate multiple damages in the sensor network and the results showed the good robustness of the algorithm.

Investigation of Different Features for Baseline-Free RAPID Damage-Imaging Algorithm Using Guided Waves Applied to Metallic and Composite Plates

In guided-wave-based damage-imaging algorithms, damage reconstruction typically involves comparing the signals with and without a defect. However, in many cases, defect-free data may not be available. Therefore, in this study, baseline and baseline-free approaches were used for damage imaging, exploiting not only the amplitude of the signal as the feature but also five additional features, namely, the amplitude of the sparse signal after deconvolution, the amplitude of the coefficients at the excitation frequency from the re-assigned short-time Fourier transform, the time of flight determined from cross-correlation, kurtosis in the time domain, and kurtosis in the frequency domain. For this study, three different plates with different types of defects were considered: a metallic plate with a notch-type artificial defect, a pultruded type of composite plate with an impact defect, and a laminate composite plate with plexiglass serving as an added mass damper artificial defect. The Reconstruction Algorithm for Probabilistic Inspection of Damage (the RAPID algorithm) was used to characterize the defects on the three plates, and the defect parameters were then quantified by creating an ellipse after thresholding.

Baseline-Free Damage Imaging of Composite Lap Joint via Parallel Array of Piezoelectric Sensors.

This paper presents a baseline-free damage imaging technique using a parallel array of piezoelectric sensors and a control board that facilitates custom combinations of sensor selection. This technique incorporates an imaging algorithm that uses parallel beams for generation and reception of ultrasonic guided waves in a pitch-catch configuration. A baseline-free reconstruction algorithm for probabilistic inspection of defects (RAPID) algorithm is adopted. The proposed RAPID method replaces the conventional approach of using signal difference coefficients with the maximum signal envelope as a damage index, ensuring independence from baseline data. Additionally, conversely to the conventional RAPID algorithm which uses all possible sensor combinations, an innovative selection of combinations is proposed to mitigate attenuation effects. The proposed method is designed for the inspection of lap joints. Experimental measurements were carried out on a composite lap joint, which featured two dissimilar-sized disbonds positioned at the lap joint's borderline. A 2D correlation coefficient was used to quantitatively determine the similarity between the obtained images and a reference image with correct defect shapes and locations. The results demonstrate the effectiveness of the proposed damage imaging method in detecting both defects. Additionally, parametric studies were conducted to illustrate how various parameters influence the accuracy of the obtained imaging results.

Multi-type damage localization using the scattering coefficient-based RAPID algorithm with damage indexes separation and imaging fusion

Guided waves-based structural health monitoring (SHM) methods have potential for practical applications, since they are sensitive to small damages and are able to realize large area monitoring. Among these methods, the Reconstruction Algorithm for Probabilistic Inspection (RAPID), using a Piezoelectric transducer (PZT) sensor array, is one of the most widely used imaging algorithms to perform active damage monitoring and localization. However, since the sensing paths are distributed inside the sensor array with the non-uniform density, the RAPID algorithm can only localize damage when it is occurring inside of the array. If the damage occurs outside of the array or both inside and outside of the array, that is, multi-type damage, the performance of RAPID algorithm would not be satisfactory. In this paper, a scattering coefficient-based RAPID algorithm with damage indexes separation and imaging fusion is proposed. The amplitude of damage scattered signal at the corresponding time of fight is adopted as the weight in the probability distribution function, and damage indexes are then classified into two categories in the RAPID algorithm for the inside and outside damage localization respectively. Finally, an experiment on the complex composite plate, with the center large hole and surrounding bolt holes, is carried out to verify this proposed method. Experimental results show that this method can realize multi-type damage localization with errors less than 40 mm.

Enhancing Lamb wave-based damage diagnosis in composite materials using a pseudo-damage boosted convolutional neural network approach

Damage diagnosis of thin-walled structures has been successfully performed through methods based on tomography and machine learning-driven methods. According to traditional approaches, diagnostic signals are excited and sensed on the structure through a permanently installed network of sensors and are processed to obtain information about the damage. Good performance characterizes methods that process Lamb waves, which are described by long propagation distances and high sensitivity to anomalies. Most of the methods require extracting damage-sensitive features from the diagnostic signals to drive the damage diagnosis task. However, this process can lead to loss of information, and the choice of the specific feature to extract may introduce biases that hamper damage diagnosis. Furthermore, traditional approaches do not perform well when composites are considered, due to the anisotropy, inhomogeneity, and complex damage mechanisms shown by this type of material. To boost the performance of methods for damage diagnosis of composite plates, this work proposes a convolutional neural network (CNN)-based algorithm that localizes damage by processing Lamb waves. Different from other methods, the proposed method does not require extracting features from the acquired signals and allows localizing damage through the regression approach. The method was tested against experimental observations of Lamb waves propagating in two composite panels and in a hybrid panel, and the performance of two different sensor arrays was investigated. The pseudo-damage approach was used to generate large enough datasets for training the CNNs, and the performance of the framework was evaluated by localizing pseudo-damage and real damage determined by low-velocity impacts. The CNN-driven method accurately localized damage in all the considered scenarios, and it also outperformed traditional damage indices-based approaches, such as the reconstruction algorithm for probabilistic inspection of defects.

Modified reconstruction algorithm for probabilistic inspection of damage based on damaged virtual sensing paths

Ultrasonic guided wave is an effective approach for structural damage detection, among which the reconstruction algorithm for probabilistic inspection of damage (RAPID) is widely used. For the problem that the localization accuracy of traditional RAPID is seriously affected by the damage shape factor β, this paper proposes a modified RAPID based on damaged virtual sensing paths (DVSPs). The DVSP is determined by the time of flight (TOF) or damage index (DI) and is defined as a line of passing damage from a real actuator to a virtual sensor. Using DVSPs instead of real paths for damage localization can effectively avoid the β selection dilemma. Experiments were conducted on an aluminum plate and composite laminates. With the change of β, the average localization error does not exceed 9 mm, and the variation is less than 2 mm, while the error variance of the traditional RAPID is more than 35 mm.

Adaptive time-reversal method for delamination detection of composite plates based on reconstruction algorithm for probabilistic inspection of defects

In this study, delamination in composite plates was detected by combining a baseline-free detection method based on a time-reversal method with a damage-imaging algorithm based on the reconstruction algorithm for probabilistic inspection of defects. A new adaptive time-reversal method (ATRM) for calculating the damage index values was proposed through the local adaptive analysis of the time-reversal signals of Lamb waves. The time-reversed signals were obtained by determining the maximum correlation between the forward path response and excitation signals. The forward path and time-reversal signals were intercepted using local optimal correlation coefficients to compensate for the peak offset errors that occurred during Lamb wave propagation. Multiple optimal adaptive decompositions were used to eliminate the interference of reflected waves in the forward path. The feasibility of the proposed method was verified numerically using finite element simulations, and was quantitatively compared with that of the conventional time-reversal method. Experimental studies using actual composite plates further validated the proposed method. The influence of the damage location on the performance of the proposed method was also investigated. The results show that the ATRM method significantly improved the temporal reversibility and obtained the optimal global similarity, making it more accurate and reliable for localising the delamination defects in composite plates compared with existing time reversal-based methods.

A time-reversal assisted probabilistic fusion strategy for damage localization in plate-like structures using Lamb waves

Precise damage identification and quantification is of great significance to ensure structural performance and provide early-warning for safety maintenance. In this research, a time-reversal assisted probabilistic strategy is developed to accurately localize damage via baseline-free manner in plate-like structures by fusing damage prediction data obtained from the elliptical trajectory location method (ETLM) and reconstruction algorithm for probabilistic inspection of defects (RAPID) algorithm. Damage features, including time difference for ETLM and correlation coefficient index (CCI) for RAPID algorithm, are extracted from the time-reversal focusing signal using the Hilbert transform (HT). To make full use of the damage-related information contained in the time-reversal focusing signals and improve the localizing accuracy, a decision-level data fusion based on Bayesian inference is proposed to fuse damage features and reconstruct damage information. A series of numerical and experimental studies, including different configurations of damage cases and transducer distributions, were conducted to investigate the performances of the proposed time-reversal assisted probabilistic method on damage localization. Results shows that the proposed method could successfully achieve accurate damage localization via baseline-free manner and significantly reduce the damage artifacts compared to traditional methods.

An Improved RAPID Imaging Method of Defects in Composite Plate Based on Feature Identification by Machine Learning.

The RAPID (reconstruction algorithm for probabilistic inspection of defect) method based on Lamb wave detection is an effective method to give the position information of a defect in composite plate. In this paper, an improved RAPID imaging method based on machine learning (ML) is proposed to precisely visualize the location and features of defects in composite plate. First, the specific feature information of the defect, such as type, size and direction, can be identified by analyzing the detection signals through multiple machine learning models. Then, according to the obtained defect features, the scaling parameter β of the RAPID method which controls the size of the elliptical area is revised, and weights are set to the important detection paths which are related to defect features to realize precise defect imaging. The simulation results show that the proposed method can intuitively characterize the location and related feature information of the defect, and effectively improve the accuracy of defect imaging.

An efficient Lamb wave-based virtual refined time-reversal method for damage localization in plates using broadband measurements

This study proposes a modified virtual time-reversal (VTR) algorithm for baseline signal-free damage detection in plate-like structures. The physical actuation and sensing of Lamb waves are performed using a broadband Gaussian excitation instead of the conventional narrowband modulated tone burst excitations. The forward response and the reconstructed signal due to the time-reversal process for a narrowband input signal are then constructed virtually using the broadband transfer function. The method eliminates the possibility of numerical errors encountered in the conventional VTR method based on narrowband excitations. It is also more efficient than the traditional VTR algorithm because it can probe at multiple excitation frequencies using a single measurement for each sensing path. This modified VTR algorithm is employed in the recently developed refined time-reversal method (RTRM), which uses an extended signal length of the reconstructed signal for computing damage index (DI) and probes the structure at the best reconstruction frequency. The new method is termed the virtual refined time-reversal method (VRTRM). The DIs based on the VRTRM are used in the reconstruction algorithm for probabilistic inspection of defects to achieve baseline signal-free localization of damages. The efficacy of the proposed VRTRM for damage localization is experimentally verified with the established technique RTRM. Experiments are performed in an aluminium plate equipped with a network of surface-bonded piezoelectric patch transducers to illustrate the conventional VTR’s pitfalls and the modified VTR’s accuracy for a single mass damage scenario. The results show that the proposed VRTRM is as accurate as the established technique RTRM in estimating the reconstructed signals and localizing a block mass damage. Finally, the VRTRM is shown to localize in a dual damage scenario with excellent accuracy. In contrast, the conventional main mode-based VTR method fails to localize the damages with or without single-mode tuning.

On the mitigation of the RAPID algorithm uneven sensing network issue employing averaging and Gaussian blur filtering techniques

Ultrasonic guided waves-based Structural Health Monitoring (SHM) is a promising solution to perform damage diagnosis in plate-like structures. In this field, the Reconstruction Algorithm for Probabilistic Inspection (RAPID) is one of the most widely used tomographic algorithms to perform active damage detection and localisation. Even though this algorithm is easily implementable, very versatile and satisfactorily accurate, it presents some disadvantages, such as the production of artefacts determined by non-uniform distributions of the sensing network density. In the present article, a processing and spatial filtering technique is proposed, which strongly mitigates the aforementioned problem. The proposed methodology is validated using a numerical database created for a large carbon fibre reinforced polymer (CFRP) with a through-the-thickness hole.

A Bayesian fusion method for composite damage identification using Lamb wave

This study presents a novel method for composite damage identification using Lamb wave. A probabilistic integration of the elliptical loci method and the RAPID (reconstruction algorithm for probabilistic inspection of defects) in a Bayesian framework is proposed. The proposed method allows for the incorporation of multiple damage sensitive features in a rational manner to improve the reliability and robustness for a given array of sensors. Numerical studies are performed to verify the effectiveness of the proposed method and to compare its accuracy with existing methods. Experimental investigation using a realistic composite plate is made to further validate the proposed method. The influence of damage location and the number of participating sensors on the performance of the proposed method is discussed. Results indicate that the proposed method yields more accurate and reliable results comparing with existing methods.

Structural health monitoring of an adhesively bonded CFRP aircraft fuselage by ultrasonic Lamb Waves

The aviation industry faces the challenge of offering aircraft that are lighter, more economical, and safer. One of the solutions is to increase the use of composites. For these materials, adhesive bonding has proven to be the appropriate joining technology. To check these adhesive joints, costly and time-consuming maintenance measures are carried out. An intelligent Structural Health Monitoring (SHM) system can extend these intervals and allow the use of a predictive maintenance system. This paper describes the method of Ultrasonic Lamb Waves for monitoring a adhesively bonded Carbon Fiber Reinforced Polymer (CFRP) aircraft fuselage. Prior to this, the production of a segment of a fuselage and the characterization of the materials (CFRP and adhesive) is shown. Afterwards the method of Ultrasonic Lamb Waves with the use of piezoelectric transducers and signal processing based on the Reconstruction Algorithm for Probabilistic Inspection of Damage (RAPID) algorithm are explained. At the end, the experimental evaluation of an undamaged and a damaged fuselage structure is done. The results have shown the possibility of RAPID algorithm for damage detection on adhesive. An outlook on future work is given.

Influence and Compensation of Temperature Effects for Damage Detection and Localization in Aerospace Composites.

Structural Health Monitoring (SHM) of Carbon Fiber Reinforced Polymers (CFRP) has become, recently, in a promising methodology for the field of Non-Destructive Inspection (NDI), specially based on Ultrasonic Guided Waves (UGW), particularly Lamb waves using Piezoelectric Transducers (PZT). However, the Environmental and Operational Conditions (EOC) perform an important role on the physical characteristics of the waves, mainly the temperature. Some of these effects are phase shifting, amplitude changes and time of flight (ToF) variations. In this paper, a compensation method for evaluating and compensating the effects of the temperature is carried out, performing a data-driven methodology to calculate the features from a dataset of typical temperature values obtained from a thermoset matrix pristine plate, with a transducer network attached. In addition, the methodology is tested on the same sample after an impact damage is carried out on it, using RAPID (Reconstruction Algorithm for Probabilistic Inspection of Damage) and its geometrical variant (RAPID-G) to calculate the location of the damage.

Accurate baseline-free damage localization in plates using refined Lamb wave time-reversal method

In a recent study, Agrahari and Kapuria (J Int Mater Sys Struct, 27, 1283–1305, 2016) presented a Lamb wave based refined time reversal method (RTRM) for baseline-free damage detection in thin plate structures. In this method, it was proposed to conduct the probe at the frequency of best reconstruction and to use an extended wave packet ranging between two side bands accompanying the main mode of the reconstructed signal for computing the damage index. The method showed excellent sensitivity to damage in a single actuator-sensor path scenario. In the present work, the RTRM is integrated with a damage imaging algorithm called the reconstruction algorithm for probabilistic inspection of defects (RAPID) to develop an accurate baseline-free damage localization technique using a network of piezoelectric wafer patch transducers. Its performance is tested experimentally in an aluminium plate with a block mass damage. It is found that the proposed RTRM conducted at the best reconstruction frequency of the sensor network-plate system is able to predict the damage location with very good accuracy, whereas the other existing baseline-free methods such as the conventional TRM with single mode tuning and the reciprocity principle based method are either ineffective in localizing or give highly erroneous prediction of the damage location.

Damage Detection and Characterization in Composites Using a Geometric Modification of the RAPID Algorithm

Structural Health Monitoring (SHM) of aircraft structures, which are manufactured more and more using composite material, is a well-established technique for increasing reliability and reducing maintenance costs. Implementing damage inspection algorithms that run in dedicated electronic systems helps reduce big data processing time with improved results. RAPID (Reconstruction Algorithm for Probabilistic Inspection of Damage) algorithm, developed some years ago, provides a technique to identify and characterize damages both in metallic and composite materials. However, this algorithm sometimes provides inaccurate predictions of the damage location, caused mainly by the influence of path intersection points among transducers, that can mask the current location of damages. This work presents a geometrical modification of standard RAPID algorithm (RAPID-G) that mitigates the influence of the intersection point between sensor paths. The results highlight that the location of damages improves significantly with this modification of the algorithm.

Boundary-enhanced reconstruction algorithm for probabilistic inspection of composite plate defects with air-coupled Lamb waves

Air-coupled Lamb wave imaging is an emerging non-contact structural defect inspection method. The current imaging algorithms still face challenges such as imaging accuracy improving. In this work, a boundary-enhanced reconstruction algorithm for probabilistic inspection of damage (BERAPID) is proposed to improve the defect boundary imaging accuracy. The improvement is through a skewed distribution algorithm to adjust the energy distribution of the Lamb wave when it passes through the defect edge. The BERAPID algorithm compensates for the problem of boundary blur and defect expansion in traditional RAPID. The composite plate defect imaging system based on air-coupled Lamb waves is built to verify the improved algorithm. The evaluation indicators of imaging error and image contrast are presented for 6 different types of defects. The experimental results demonstrate that the BERAPID can effectively reduce the imaging error and improve the image contrast for defect boundaries.

A study on guided wave tomographic imaging for defects on a curved structure

Guided wave tomography is a very attractive technique for online nondestructive evaluation and structural health monitoring. The reconstruction algorithm for probabilistic inspection of damage (RAPID) is an effective tomography algorithm for detecting, locating, and imaging defects. Conventional tomography imaging techniques are difficult to quantify defects in structures with curved surfaces like aircraft. It is not a complete tool for evaluation of a damaged area on a curved surface, because of insufficient guidelines for shape factor (Bata) and curvature. Probabilistic inspection of damage is used to construct tomographic images of hole defects in a 30 mm diameter and thickness 5 mm carbon steel plate and curved surface specimen. Imaging is completed using an array of 16 transducers. It is shown that defect location can be accurately determined on plate and curved surfaces. The work presented here introduces a calculation for the shape factor for evaluation of the damaged area, as well as a variable $$\beta$$ parameter technique to correct a damaged shape. An experiment is performed using the guided wave pitch-catch method to find the length of the damage on a ray path. Also, we perform research in modeling simulation and an experiment for comparison with a suggested inspection method and verify its validity. In the plate and curved surface images resulting from the advanced RAPID algorithm, the defect area and shape showed good agreement. Quantitative imaging techniques on the surface can be applied to real-time defects imaging of aircraft component monitoring.