Accuracy Of Damage Localization Research Articles

A damage localization technique using wave front shapes in composite laminates without knowing the velocity profile

Composite laminates are widely used in various fields, but their structures are prone to cracks and damage. Due to the difference in angles of the instantaneous direction of the wave front propagation and the direction of the energy flow in an anisotropic material, the use of Lamb waves for damage localization in composite laminates is a challenging task. Establishing the wave front shape equation can overcome the difficulty of damage localization caused by anisotropy, but this usually requires a priori knowledge of the acoustic velocity distribution of the laminates, which is not convenient for efficient damage localization. In this paper, a damage localization method based on wave front shapes for composite laminates without any knowledge of the velocity profile is presented. Numerical simulation and experimental results show that the proposed method works. This method shows good damage localization accuracy and has broad application prospects in non-destructive testing for plate structures with strong anisotropy.

Enhancing Damage Localization in GFRP Composite Plates: A Novel Approach Using Feedback Optimization and Multi-Label Classification

Damage localization in GFRP (glass-fiber-reinforced polymer) composite plates is a crucial research area in marine engineering. This study introduces a feedback-based damage index (DI) combined with multi-label classification to enhance the accuracy of damage localization and address scenarios involving multiple damages. The research begins with the creation of a modal database for yachts’ GFRP composite plates using finite element modeling (FEM). A method for deriving a feedback-weighted matrix, based on the accuracy of the DI, is then developed. Sensitivity analysis reveals that the feedback DI is 50% more sensitive than the traditional DI, reducing false positives and missed detections. The associated feedback-weighted matrix depends solely on the structural shape, ensuring its transferability. To address the challenge for localizing multiple damages, a multi-label classification approach is proposed. The synergy between the feedback optimization and multi-label classification enables the rapid and precise localization of multiple damages in GFRP composite plates. Modal testing on damaged GFRP plates confirms the enhanced accuracy for combining the feedback DI with multi-label classification for pinpointing damage locations. Compared with traditional methods, this feedback DI method improves sensitivity, while multi-label classification effectively handles multiple damage scenarios, enhancing the overall efficiency of the damage diagnosis. The effectiveness of the proposed methods is validated through experimentation, offering robust theoretical support for composite plate damage diagnostics.

Unsupervised data-driven method for damage localization using guided waves

Researchers have recently gained interest in ultrasonic guided waves (UGWs) for structural health monitoring (SHM). This interest has mainly grown thanks to the high sensitivity of UGWs to changes in the mechanical and material properties of the medium they travel in. Among the several types of UGWs, Lamb waves (LWs) have been successfully employed to detect, localize and quantify damage in thin-walled structures. To date, localization has been mainly performed through tomographic algorithms. Although those algorithms represent consolidated methods, they come with unsolved issues, such as artifacts generation in damage probability maps and strong dependency on the network of sensors installed to excite and sense diagnostic signals. Hence, more recently, supervised data-driven approaches have been propounded in the literature for LW-based damage diagnosis. Despite the good performance of such methods, a strong limitation is represented by the need for large high-fidelity datasets for training, which are generally not available for real-life structures. A possible solution is represented by unsupervised machine learning methods. Such methods have already been employed for damage detection in the SHM field, but no fully unsupervised approach to damage localization has been proposed yet. Hence, in this work, a framework combining an unsupervised data-driven method and an in-house tomographic approach is presented to process LWs to localize damage. Specifically, convolutional auto-associative neural networks (CAANNs) are employed to process diagnostic signals without requiring any prior feature extraction process. Furthermore, CAANNs predictions are processed to generate damage probability maps. The performance of the proposed method was tested against a numerical case study involving an Aluminum plate and against two experimental datasets of LWs acquired on a full-scale composite wing and a composite plate. Results showed that the proposed method outperformed classic damage diagnosis algorithms in terms of damage localization accuracy.

High accuracy and resolution damage detection for concrete based on novel virtual correlation imaging method and embedded piezoelectric sensing technology

This paper presents a novel virtual correlation imaging (VCI) method to reduce the adverse effects of stress wave distortion and realize high accuracy and resolution damage imaging based on the sparse stress wave data collected by embedded piezoceramic transducers. Specifically, a compensation function is first constructed in the VCI method based on the frequency response characteristic of damage-induced scattering signals. Then, the constructed compensation function is emitted as the virtual excitation signal in the simulation space via virtual channels. To automatically compensate for the effect of waveform distortion, a virtual correlation operator is developed based on the scattering signal and its corresponding virtual received signal to design the imaging function. Finally, the practicability of the proposed VCI method is investigated on a concrete specimen with two successively drilling holes under laboratory conditions. The results indicate that, compared with the conventional damage imaging methods, the proposed VCI method can effectively decrease the adverse effect of distorted probing stress waves on damage localization accuracy and spatial resolution during the imaging process.

Damage localization and robust diagnostics in guided-wave testing using multitask complex hierarchical sparse Bayesian learning

The inversion of guided-wave data for accurate damage localization is a challenging problem when using guided waves for nondestructive testing and robust diagnostics. It is especially important to detect incorrect damage localization without knowing the original damage. In this paper, a new damage localization and robust diagnostics method is proposed. A Multi-task Complex Hierarchical Sparse Bayesian learning (MuCHSBL) algorithm is presented to solve the inverse problem for damage localization based on the data measured from a small number of sensors. The multi-task model improves the efficacy of damage localization by utilizing the consistency of damage locations for tasks with different signal frequencies. A Sparse Bayesian learning algorithm is also introduced to utilize the spatial sparsity of the damage, since structural damage typically occurs at only a few localized areas. The quantified posterior uncertainty of the model parameters gives a sense of confidence in the damage localization results. Utilizing the different levels of uncertainty in the optimal and suboptimal inversion models, diagnostic tools are proposed to detect whether the inversion for damage localization is accurate, without knowing the original damage. Numerical and experimental studies are carried out to verify the effectiveness of the proposed method. It is demonstrated that the damage localization efficacy of multi-task model is much higher than that of single-task model; moreover, the accuracy of damage localization results can be diagnosed effectively by the posterior uncertainty quantification of the model parameters.

Damage localization for composite structure using guided wave signals with Gramian angular field image coding and convolutional neural networks

Accurate detection of damage in composite structures is of great significance to ensure safe service and avoid catastrophic accidents. In this paper, a novel damage diagnosis method, integrating guided wave (GW)-based structural health monitoring (SHM) with Gramian angular field (GAF) image coding and convolutional neural networks (CNNs), is proposed to improve the accuracy of damage localization in composite structures. Firstly, an improved piecewise aggregate approximation (PAA) algorithm is proposed to achieve guided wave data compression and obtain a series of damage indexes (DIs) characterizing the damage for effectively improving the localization accuracy. Then, the multi-path fused DI sequences are converted into two-dimensional (2-D) images using GAF to take full advantage of the significant benefits of CNN in machine vision. Secondly, a CNN model is constructed to learn high-level feature representations and conduct damage location regression. Finally, the proposed method is tested and assessed on measured GW signal datasets with the addition of zero-mean Gaussian noise. The experimental results show that the mean relative error of the proposed method for damage localization on the invisible datasets is 3.59%. The proposed method demonstrates better generalization and localization performance than other selected state-of-the-art damage localization techniques.

Damage localization using electromechanical impedance technique based on inverse implementation

Structural Health Monitoring (SHM) systems allow maintenance planning which reduces maintenance costs. These systems may also allow lifetime extension of structures which allows reduction in lifecycle costs of structures. As a result, SHM systems have been of interest to researchers in civil and mechanical engineering sectors. The SHM system should be ideally low cost, suitable for continuous monitoring, and able to detect small levels of damage. The sensitivity to small levels of damage is even more important for composite structures as composites have low damage tolerance. Electromechanical Impedance (EMI)-based techniques have been proposed due to their sensitivity to low levels of damage. Furthermore, due to their high frequency of operation they are immune to ambient noise. Most of the studies using the EMI technique were limited to damage detection only. Recently some studies have used a network of sensors for EMI measurements and used them for damage localization. This paper follows on this trend of using a network of sensors for damage localization using EMI technique. A novel inverse algorithm is proposed for the damage localization. For the inverse technique, an analytical model for the correlation of distance and angle with the damage index (DI) is developed. This model is then used to generate a database of the DIs for a large number of damage scenarios. The measured DIs from the structure are then compared with this database and the scenario with the least mean error in the DIs is the predicted damage scenario. The algorithm takes into consideration the modeling errors and the measurement errors through regularization and in turn allows more accurate damage localization. The method is applied on different damage scenarios on an unidirectional composite sample. Some sensitivity studies such as the performance of the method using different fits have been carried out. In addition, the accuracy of damage localization of the proposed technique has been compared to a localization technique found in literature. The results show that indeed the proposed algorithm works and improves the damage localization using the EMI technique.

Damage localization method for plate-like composite structure based on valid path optimization and search point matching

In order to improve the damage localization accuracy and efficiency of composite structures, an improved energy positioning method combining valid path optimization and search point matching is proposed in this paper. Firstly, the damage index (DI) can be calculated according to the healthy and damaged signals, and the valid sensing paths are then obtained by combining the DI threshold set based on the healthy signal. Then, the ineffective search points are filtered out by comparing the time-of-flight (TOF) of search points with that of the damage. Finally, the accumulated energy for each effective search point can be calculated based on its TOF and the scattered signals on valid sensing paths. The geometric center of the region with the greatest energy concentration is considered as the damage position. The performance of the proposed method was verified by constructing experimental system. Experimental result shows that for 450 mm × 450 mm × 2.4 mm and 800 mm × 800 mm × 2.4 mm composite laminates, the average absolute positioning errors obtained by the proposed method are 2.69 mm and 5.00 mm respectively, and the maximum positioning errors are 8.85 mm and 11.66 mm respectively. This study can provide a new idea for damage detection in composite structures.

A new damage detection method of single-layer latticed shells based on combined modal strain energy index

The existing damage detection methods are challenging to be directly applied to single-layer latticed shells due to the complex mechanical mechanism and modal characteristics of such structures. In this regard, an effective damage detection method based on the combined modal strain energy index is proposed in this paper to detect the location of the damaged members in such structures. Based on the vibration modal features before and after damage of structures, the modal matching and combination method is presented to generate the combined mode, which include the modal matching procedure based on the modal assurance criterion and the modal combination procedure using the Kriging interpolation. Compared with the damaged mode, the specially constructed combined mode can significantly reduce the mode shape discrepancy in the undamaged region on the basis of retaining the characteristic differences in the damaged members. On this basis, the combined modal strain energy change index is proposed according to the modal strain energy difference between the original mode and the combined mode. Since the proposed index is established according to the difference between the combined vibration mode and the original vibration mode, the values of damage index of the undamaged members are greatly reduced, so as to effectively avoid the occurrence of misjudgement. Several numerical examples with different damage scenarios are studied in order to evaluate the proposed approach. The results clearly indicate that the proposed method features high damage localization accuracy compared with traditional methods and good measurement noise robustness.

Damage localization in reinforced concrete beams strengthened with FRP sheets using modal strain energy method

Civil structures are affected by many different factors from the environment, loads, aging of materials, … These factors are uncertain variables and affect the health of the structures. Therefore, structural health monitoring (SHM) is very essential to detect damages early for necessary maintenance. In this paper, damaged locations in reinforced concrete beams strengthened with FRP (Fiber Reinforced Polymer) sheets are identified by using the modal strain energy method. First, a reinforced concrete beam strengthened with FRP sheets is simulated by ANSYS APDL software in order to analyze the beam’s behavior and get vibration responses. The reliability of the simulation is verified by comparing the load - displacement relationship between numerical and experimental results. Next, the modal strain energy method is employed to determine the damaged locations (crack and debonding) in the beam. In which, a set of indicators to evaluate the accuracy of damage localization results is proposed. The feasibility of the method is demonstrated through two problems. For problem 1, five different damage scenarios including concrete damage and FRP debonding are examined to evaluate the modal strain energy method’s feasibility for damage detection in the target beam. For problem 2, damages occurring in the beams are analyzed and determined according to each load level corresponding to the real working of the target beam. The results show that the modal strain energy method has high accuracy in detecting and locating damages in reinforced concrete beams strengthened with FRP sheets.

Probability-based diagnostic imaging with corrected weight distribution for damage detection of stiffened composite panel

Due to no requirement for direct interpretation of the guided wave signal, probability-based diagnostic imaging (PDI) algorithm is especially suitable for damage identification of complex composite structures. However, the weight distribution function of PDI algorithm is relatively inaccurate. It can reduce the damage localization accuracy. In order to improve the damage localization accuracy, an improved PDI algorithm is proposed. In the proposed algorithm, the weight distribution function is corrected by the acquired relative distances from defects to all actuator–sensor pairs and the reduction of the weight distribution areas. The validity of the proposed algorithm is assessed by identifying damages at different locations on a stiffened composite panel. The results show that the proposed algorithm can identify damage of a stiffened composite panel accurately.

Pile damage identification method for high-pile wharfs based on axial static strain distribution

This paper proposes a damage identification method for piles by using two symmetrically arranged strain sensor arrays. Theoretical analysis shows that the curvature change have a significant change in the damaged region and can be used for damage localization, the relative curvature change in the damaged region is a constant and can be used to quantitative the severity of damage. The physical model experiments showed that the predetermined damage was located within the measured damage range, and the cross section damage coefficient had a linear correlation to the measured damage severity. The FEM results showed that the maximum relative error of damage severity is 3.3%, and the maximum error of damage location is 1 mm, the damage localization accuracy could be improved by increasing the strain measurement point density. The method is simple, does not rely on optimization algorithms, and is suitable for pile damage detection and monitoring.

A new acoustic emission damage localization method using synchrosqueezed wavelet transforms picker and time-order method

Acoustic emission technique, as a passive structural health monitoring technique, has been widely applied to detecting and locating the structural damage. The time difference of arrival and the wave velocity are the key factors in most of the acoustic emission localization methods, and the accuracy of these two factors will affect the accuracy of damage localization. To improve the accuracy of damage localization, this article proposes a new damage localization method based on the synchrosqueezed wavelet transform picker and the time-order method. The synchrosqueezed wavelet transform picker, which picks the time–frequency similar point based on time–frequency similarity theory in the low-noise interval of time–frequency matrix, can improve the accuracy and robustness of calculating time difference of arrival. Meanwhile, the time-order method not only measures the wave velocity in real time but also reduces the computing time by appropriately arranging the distribution of acoustic emission sensors. These advantages improve the accuracy and robustness of acoustic emission localization, which was verified by experiments. Furthermore, the new localization method was employed to study the energy distribution in the embedded section of steel bar during the pull-out test of steel bar and concrete, and the results show the types of resistance between steel bar and concrete.

An inverse elastic method of crack identification based on sparse strain sensing sheet

Reliable damage detection over large areas of structures can be achieved by spatially quasi-continuous structural health monitoring enabled by two-dimensional sensing sheets. They contain dense arrays of short-gauge sensors, which increases the probability to have sensors in direct contact with damage (e.g. crack opening) and thus identify (i.e. detect, localize, and quantify) it at an early stage. This approach in damage identification is called direct sensing. Although the sensing sheet is a reliable and low-cost technology, the overall structural health monitoring system that is using it might become complex due to large number of sensors. Hence, intentional reduction in number of sensors might be desirable. In addition, malfunction of sensors can occur in real-life settings, which results in unintentional reduction in the number of functioning sensors. In both cases, reduction in the number of (functioning) sensors may lead to lack of performance of sensing sheet. Therefore, it is important to explore the performance of sparse arrays of sensors, in the cases where sensors are not necessarily in direct contact with damage (indirect sensing). The aim of this research is to create a method for optimizing the design of arrays of sensors, that is, to find the smallest number of sensors while maintaining a satisfactory reliability of crack detection and accuracy of damage localization and quantification. To achieve that goal, we first built a phase field finite element model of cracked structure verified by the analytical model to determine the crack existence (detection), and then we used the algorithm of inverse elastostatic problem combined with phase field finite element model to determine the crack length (quantification) and location (localization) by minimizing the difference between the sensor measurements and the phase field finite element model results. In addition, we experimentally validated the method by means of a reduced-scale laboratory test and assessed the accuracy and reliability of indirect sensing.

Structural Health Monitoring of Pressure Vessel Based on Guided Wave Technology. Part II: Damage Localization Accuracy Influencing Factors

Structural Health Monitoring of Pressure Vessel Based on Guided Wave Technology. Part II: Damage Localization Accuracy Influencing Factors

A modified damage index probability imaging algorithm based on delay-and-sum imaging for synthesizing time-reversed Lamb waves

Imaging for damage in plate structure by Lamb waves is one of the most effective methods in the field of structural health monitoring. In order to improve the accuracy of damage localization, a novel method is proposed to modify damage exponent probability imaging algorithm based on delay-and-sum imaging by using time reversal Lamb waves. A new probability distribution function is introduced to improve the damage index probability method and is combined with delay-and-sum method for damage localization. Experimental results on aluminum plate show that the hybrid algorithm achieves better accuracy of damage location and imaging quality than the conventional delay-and-sum method.

Reference-free damage localization in time-space domain for structural health monitoring of X-COR sandwich composites

This article presents a guided wave based damage localization framework using a time-space analysis for structural health monitoring of X-COR sandwich composites with a reference-free perspective to overcome the difficulty in detecting reflected guided waves in a highly attenuated media. Transducers, including macro-fiber composites and piezoelectric wafers, are used to design the sensing paths. The time-space domain is constructed using de-noised signals that are processed by signal processing techniques including matching pursuit decomposition and Hilbert transform. The localization framework is then validated across a wide range of excitation frequencies in X-COR sandwich composites with seeded facesheet delamination. The results indicate that time-space analysis offers a high accuracy for detection and localization of internal damages and serves as a promising framework for structural health monitoring of complex sandwich composites with reinforcements. This work also provides a comprehensive study of the changes in group velocities, attenuation tendencies, and time-space resolution of actuated and converted modes under different excitation frequencies across a range of ultrasonic transducer sizes, thereby helping to improve reliability and accuracy of damage localization in time-space domain.

Optimization of piezoelectric wafer placement for structural health-monitoring applications

Optimization of the number and location of piezoelectric wafers used in sensor networks for continuous monitoring of automotive and aerospace structures is not yet fully developed. This article presents a novel approach for the optimization of piezoelectric wafer networks for application in the field of structural health monitoring. A mixed integer nonlinear program is formulated and validated for different geometric shapes. The proposed objective function is to maximize the coverage of the area under study, described by a set of control points. In the optimal solution, each control point should be covered by a user-defined number of sensing paths, where each sensing path is the line joining any two piezoelectric transducers within the monitored plate. During the optimization process, any place on the plate is a potential location of a piezoelectric wafer. A mathematical programming language is used to code the algorithm, and selected simulation cases are executed to demonstrate the efficiency of the proposed optimization algorithm. The algorithm introduces several optimization parameters, such as coverage Levels 2 and 3, number of paths passing through a control point, and areas of concentrated coverage. Furthermore, the algorithm features the flexibility to change a wide range of problem parameters, such as the number of piezoelectric wafers, their coverage range, and the number of control points. The tractability of the model proposed is improved by feeding the solver an initial solution that makes the branch-and-bound technique less extensive. Data fusion is conducted to determine the quality of the coverage provided by the optimized piezoelectric wafer locations and to scrutinize the efficiency of the proposed approach. Finally, two sensor network configurations are selected and validated experimentally. The validation demonstrates a high level of coverage within the network, as well as evaluating the accuracy of damage localization within the optimized sensor networks.

An Embedded Tubular PZT Transducer Based Damage Imaging Method for Two-Dimensional Concrete Structures

In this paper, a damage imaging method for 2-D concrete structures is proposed by employing embedded tubular piezoceramic transducers. The proposed method can image the damages when few numbers of transducers are used or less experimental data is collected. First, the time difference of arrival-based delay and sum (DAS) imaging method is adopted to improve the damage localization accuracy for concrete structural health monitoring. Compared with the conventional DAS imaging algorithm, the proposed method increases the potential number of combinations between the response signals induced by the damage in each transmitter and sensor pair, improving the signal to noise ratio of the imaging map and enhancing the performance of the defect detection and location. Second, to enable the proposed method applicable to a 2-D concrete structure (such as cement concrete pavement and shear walls), embeddable tubular lead zirconate titanate (PZT) transducers rather than conventional PZT patches are used. The new tubular PZT transducers are able to generate radially uniform stress waves in a planar concrete structure. Due to the special construction of the tubular PZT transducers, the detection aperture is significantly improved. Therefore, a few transducers are needed in contrast with the conventional PZT patches. The experimental results conducted on a concrete slab with eight embedded tubular PZT transducers demonstrate that the proposed method can predict the defect accurately and efficiently even with few transducers and less experimental data.

A systematic method from influence line identification todamage detection: Application to RC bridges

Ordinary reinforced concrete (RC) and prestressed concrete bridges are two popular and typical types of short- and medium-span bridges that accounts for the vast majority of all existing bridges. The cost of maintaining, repairing or replacing degraded existing RC bridges is immense. Detecting the abnormality of RC bridges at an early stage and taking the protective measures in advance are effective ways to improve maintenance practices and reduce the maintenance cost. This study proposes a systematic method from influence line (IL) identification to damage detection with applications to RC bridges. An IL identification method which integrates the cubic B-spline function with Tikhonov regularization is first proposed based on the vehicle information and the corresponding moving vehicle induced bridge response time history. Subsequently, IL change is defined as a damage index for bridge damage detection, and information fusion technique that synthesizes ILs of multiple locations/sensors is used to improve the efficiency and accuracy of damage localization. Finally, the feasibility of the proposed systematic method is verified through experimental tests on a three-span continuous RC beam. The comparison suggests that the identified ILs can well match with the baseline ILs, and it demonstrates that the proposed IL identification method has a high accuracy and a great potential in engineering applications. Results in this case indicate that deflection ILs are superior than strain ILs for damage detection of RC beams, and the performance of damage localization can be significantly improved with the information fusion of multiple ILs.