Probability-based Diagnostic Imaging Research Articles

Full-field monitoring of the resin flow front and dry spot with noninvasive and embedded piezoelectric sensor networks

Vacuum-assisted resin infusion (VARI) process has been proven to be an effective-cost technology for manufacturing large-scale composite structure with complex geometries and has a broad development prospect in industry fields, especially in aerospace. The resin impregnation, a crucial stage of the VARI process, can easily lead to dry spots and seriously reduce product quality or even lead to scrap due to improper resin flow. The non-invasive piezoelectric sensor (NPS) network and embedded piezoelectric sensor (EPS) network composed of Stanford Multi-actuator Receiver Transducer Layer are employed for full-field monitoring of the resin flow fronts and accurate localization of the dry spot defects. Also, an innovative experimental technique for creating dry spot defects is presented in this research. Based on the analysis of the Lamb wave signals, the A0 mode from the NPS network and the S0 mode of the EPS network are ideal for the monitoring of dry spots. Additionally, by fusing the impact of dry spot defects on the Lamb wave and the probability-based diagnostic imaging algorithm, a new damage index is proposed to precisely locate the dry spot defects. The experimental results further indicate that the method proposed can provide effective full-field monitoring of the resin flow front and dry spot defect locations.

Lamb mode and damage identification using small-sample dictionary algorithm

ABSTRACT In this paper, Lamb mode identification method based on small-sample dictionary algorithm is proposed and applied for the separation of specific Lamb modes, the reconstruction of Lamb waves upon propagating a certain distance and damage identification. This approach includes the creation of small-sample dictionary and querying procession in a dictionary. Firstly, Lamb wave signals upon propagating at a series of distances are simulated, and signal features, {mode, distance, time of flight (Tof), wavelet energy}, are extracted to create a dictionary; secondly, Tof of the received signal is extracted, and then Lamb modes are identified by searching the dictionary; finally, energy parameters are estimated to reconstruct wavepackets. The feasibility of this algorithm is validated in AAA laminate, and the results are presented. In a 2D-simulation model of a pitch-catch configuration, A0 and S0 modes can be identified and reconstructed effectively when the direct waves and the reflected waves are synchronously received, with the propagation distance of 0.3 m and 0.5 m, respectively. In addition, a Lamb-wave-based delamination location is conducted in three-dimensional AAA laminate. The experimental results show that the delamination can be located relatively by combining the identified damage-scattered S0 waves and the probability-based diagnostic imaging.

Improving Probability-Based Diagnostic Imaging Algorithm by Modeling Weight Distribution Coefficient for Instantaneous Baseline Damage Identification

This study used a combination of the instantaneous baseline method and a probability-based diagnostic imaging (PDI) algorithm for damage identification in a stiffener-reinforced aluminium plate. Selecting an appropriate weight distribution function plays a significant role in the PDI algorithm by expressing the probability of damage existence around a sensing path. Weight distribution functions are highly dependent on the weight distribution coefficient (β). This parameter (β) normally is estimated by trial and error, which is incorrect. In addition, determining this parameter is quite costly and time-demanding. To address these issues thoroughly, a new approach is proposed for modelling the weight distribution coefficient based on the physics of wave propagation and the actuator–sensor distance. An appropriate value for β is estimated for each sensing path between actuators and sensors in a specific structure based on the proposed model, making the results more accurate and reasonable. The proposed model was validated by identifying damages at three different locations of a structure for five scenarios (three single-damage and two multiple-damage scenarios). Moreover, the results obtained from proposed model using linear and Gaussian weight distributions were compared with the results extracted from an existing approach. The acceptable localization results clearly indicate the effectiveness of proposed approach.

Assessment of Damage in Composite Pressure Vessels Using Guided Waves.

This paper deals with guided wave-based structural health monitoring of composite overwrapped pressure vessels adopted for space application. Indeed, they are well suited for this scope thanks to their improved performance compared with metallic tanks. However, they are characterized by a complex damage mechanics and suffer from impact induced damage, e.g., due to space debris. After reviewing the limited progress in this specific application, the paper thoroughly covers all the steps needed to design and verify guided wave structural health monitoring system, including methodology, digital modelling, reliability, and noise estimation for a correct decision-making process in a virtual environment. In particular, propagation characteristics of the fundamental anti-symmetric mode are derived experimentally on a real specimen to validate a variety of finite element models useful to investigate wave interaction with damage. Different signal processing techniques are demonstrated sensitive to defect and linearly dependent upon damage severity, showing promising reliability. Those features can be implemented in a probability-based diagnostic imaging in order to detect and localized impact induce damage. A multi-parameter approach is achieved by metrics fusion demonstrating increased capability in damage detection with promising implication in enhancing probability of detection.

Determination of Detection Probability and Localization Accuracy for a Guided Wave-Based Structural Health Monitoring System on a Composite Structure

The capabilities of detection and localization of damage in a structure, using a guided wave-based structural health monitoring (GWSHM) system, depend on the damage location and the chosen sensor array setup. This paper presents a novel approach to assess the reliability of an SHM system enabling to quantify localization accuracy. A two-step technique is developed to combine multiple paths to generate one probability of detection (POD) curve that provides information regarding the detection capability of an SHM system at a defined damage position. Moreover, a new method is presented to analyze localization accuracy. Established probability-based diagnostic imaging using a signal correlation algorithm is used to determine the damage location. The resultant output of the localization accuracy analysis is the smallest damage size at which a defined accuracy level can be reached at a determined location. The proposed methods for determination of detection probability and localization accuracy are applied to a plate-like CFRP structure with an omega stringer with artificial damage of different sizes at different locations. The results show that the location of the damage influences the sensitivity of detection and localization accuracy for the used detection and localization methods. Localization accuracy is enhanced as it becomes closer to the array’s center, but its detection sensitivity deteriorates.

Baseline-Free Damage Diagnostic Imaging Approach Relying on the Extraction of Converted Modes of Ultrasonic Guided Waves

Large-scale plate-like structural components have been widely used in various aerospace structures. Subject to complex loading conditions and environmental factors, the integrity of plate-like structures is threatened by the occurrence and accumulation of structural damages, calling for the development of structural health monitoring (SHM) methods and techniques. Guided wave-based SHM methods have been demonstrated with a high sensitivity to small-scale damage. In particular, the probability-based diagnostic imaging (PDI) algorithm is able to achieve enhanced accuracy and efficiency of damage localization. Typically, the PDI defines damage index (DI) in terms of the deviation between the baseline and real-time guided wave signals. However, as the baseline signals are highly sensitive to environmental factors, such as temperature, the DI may exhibit large instability subject to varied environmental factors and lead to inaccurate damage detection results. In this study, a novel damage diagnostic imaging approach free of a baseline signal is presented for nonpenetrating damages. The approach is established based on the modification of conventional baseline-dependent PDI, into which the converted mode extraction (CME) strategy is integrated to give rise to the DI in terms of the energy of the converted modes. As the essential part of the CME-PDI approach, the converted modes caused by nonpenetrating damages as structural thickness variation are extracted from the primitive multimode wave signals according to a recently proposed CME method featured by intrinsic basis mode reconstruction. The effectiveness of the approach is first examined numerically, and then an experiment is carried out by taking into account the temperature effect. Compared with conventional PDI, the CME-PDI approach shows apparently enhanced accuracy and stability in damage localization.

Localization and Imaging of Micro-Cracks Using Nonlinear Lamb Waves with Imperfect Group-Velocity Matching

Nonlinear Lamb waves have attracted increasing attention for detecting and identifying microstructural changes in structural health monitoring. However, most identification methods that determine the damage locations based on the intersections of the elliptical loci will inevitably cause positioning errors due to the change of the group velocity before and after interaction with the damage. In this work, a method focusing on elliptical rings was proposed for localization and imaging of micro-cracks in a three-dimensional structure using nonlinear Lamb waves with imperfect group-velocity matching. The width of the elliptical rings can be determined by the degree of the group-velocity mismatching of nonlinear S0 modes. The mode pair S0-s0, satisfying approximate group-velocity matching, is mainly introduced by interacting with the micro-crack. The effectiveness of the proposed methodology for damage localization is verified by the experimental testing and numerical simulation. Although the length of the being-tested small crack (about 1 mm) is smaller than the wavelength of the incident fundamental Lamb wave (around 20 mm), it can be well identified and localized using nonlinear Lamb waves. The experimental results show that the proposed method enables more reliable localization of the small crack with the crossover areas, as compared with the intersections based on the ellipse method. Furthermore, a breathing crack not situated in the propagation path can also be well localized by the proposed method in comparison with those by the probability-based diagnostic imaging in the simulation cases.

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.

Probability-Based Diagnostic Imaging Method of Fatigue Damage for Carbon Fiber Reinforced Plastic Based on ToF Damage Factor

Probability-Based Diagnostic Imaging Method of Fatigue Damage for Carbon Fiber Reinforced Plastic Based on ToF Damage Factor

Optimal sensors layout design based on reference‐free damage localization with lamb wave propagation

This study presents a new approach for designing optimal sensors layout based on accuracy of defect mapping. It is obtained from combination of the reference-free damage detection technique and the probability-based diagnostic imaging method. Considering damage indices based on continuous wavelet transform of sensors signals, the core of this study involves with development of a database of continuous wavelet transform features of a crack. In fact, the database contains the data from 594 different states in crack positions, orientations, and the considered sensing path lengths. Eventually, this database is used for localization of damage by interpolating the stored data collected from the above mentioned 594 states of the crack. The verification tests of defect mapping interpolating the stored data show an excellent match with the finite element method crack positioning performed for three different crack conditions. To generalize the results, here, 134 sensor layouts are considered in each of which 121 crack positions are detected by the help of probability-based diagnostic imaging method. Then to check upon the accuracy of the results, the distance between the real center of the crack and the detected crack position by the interpolation of the stored data is calculated. Finally, the optimal sensor layouts single out from among the 134 ones based on user-defined accuracy in localizing damage on an arbitrary monitoring area and the number of sensors.

An Aircraft Pallet Damage Monitoring Method Based on Damage Subarea Identification and Probability-Based Diagnostic Imaging

To improve the safety and economy of aircraft pallet use, an aircraft pallet damage monitoring method based on damage subarea identification and probability-based diagnostic imaging is proposed. In the proposed method, first, the large aircraft pallet monitoring area is divided into rectangular subareas, and a piezoelectric transducer sensor is pasted on each vertex of the rectangular subarea that is used to excitation and sensing the Lamb wave. Second, the damage subarea is identified according to the diagonal damage indexes. Third, the damage position in the damage subarea is calculated using the probability-based diagnostic imaging method and coordinate probability weighted algorithm. Finally, the aircraft pallet damage can be localized based on the damage subarea position. Frequency selection and damage simulation study results show that the Lamb wave is sensitive to aircraft pallet damage whose centre frequency ranges from 50 kHz to 150 kHz, and the damage index of a steel ball is less than that of all real aircraft pallet damage from 95 kHz to 125 kHz. The verification results show that the proposed method can locate aircraft pallet damage with an error of less than 2 cm.

Multi-Mode Guided Waves Based Reference-Free Damage Diagnostic Imaging in Plates

Probability-based diagnostic imaging (PDI) is one of the most well-known damage identification methods using guided waves. It is usually applied to diagnose damage in plates. The previous studies were dependent on the certain... | Find, read and cite all the research you need on Tech Science Press

Structural Health Monitoring system based on a concept of Lamb wave focusing by the piezoelectric array

Abstract Piezoelectric transducer arrays are utilized in Structural Health Monitoring systems as a means for excitation and sensing of elastic waves. Anomalies of propagating waves have enabled to develop damage detection algorithms. Depending on actuation-sensing strategies these algorithms can be classified as pitch-catch and pulse-echo. Despite many signal processing methods such as delay-sum, time-reversal, probability-based diagnostic imaging, etc. the spatial damage information provided by the actuator-sensor paths to reconstruct the damage image is limited. A novel strategy based on Lamb wave focusing is proposed in order to increase damage imaging resolution. In the proposed method all actuators are used at the same time exciting specially designed waveforms so that inspect one specific point of the structure. Damage map is created by applying appropriate signal processing. It uses dispersion curve of selected Lamb wave mode for dispersion compensation. The dispersion curve is acquired by using laser scanning Doppler vibrometer. The damage indicator is calculated based on the energy of compensated signals registered by sensors. It is shown that apart from high energy level at excitation point, energy is concentrated exactly in the damaged region. An example of crack detection and visualization in an aluminum plate is shown confirming the accuracy of the proposed method. Also the proposed method is compared to well-established delay-and-sum algorithm.

Elliptical ring distribution probability-based damage imaging method for complex aircraft structures

In engineering applications, the robustness and effectiveness of damage diagnostic imaging for guided wave-based structural health monitoring could be affected by the complexity of structures. In this study, an elliptical ring distribution probability-based diagnostic imaging algorithm is proposed to mitigate this effect using the estimated wave velocity and damage index. This algorithm improves the ability of damage localization by modifying the defect distribution probability of probability-based diagnostic imaging. The elliptical ring distribution probability of the presence of defect is used for each sensing path in the algorithm. The width of the elliptical ring distribution probability is determined by the range of estimated wave velocity. The amplitude of the elliptical ring distribution probability is determined by the damage index. The damage location is determined by the cross region of different elliptical rings for different sensing paths. The capability of the algorithm is validated by identifying damages at different locations on a complex composite fuselage panel. The results show that the proposed algorithm can identify a single damage accurately and it can identify multiple damages effectively as well.

A sparse sensor network topologized for cylindrical wave-based identification of damage in pipeline structures

A sparse sensor network, based on the concept of semi-decentralized and standardized sensing, is developed, to actively excite and acquire cylindrical waves for damage identification and health monitoring of pipe structures. Differentiating itself from conventional ‘ring-style’ transducer arrays which attempt to steer longitudinal axisymmetric cylindrical waves via transducer synchronism, this sparse sensor network shows advantages in some aspects, including the use of fewer sensors, simpler manipulation, quicker configuration, less mutual dependence among sensors, and an improved signal-to-noise ratio. The sparse network is expanded topologically, aimed at eliminating the presence of ‘blind zones’ and the challenges associated with multi-path propagation of cylindrical waves. Theoretical analysis is implemented to comprehend propagation characteristics of waves guided by a cylindrical structure. A probability-based diagnostic imaging algorithm is introduced to visualize damage in pixelated images in an intuitive, prompt, and automatic manner. A self-contained health monitoring system is configured for experimental validation, via which quantitative identification of mono- and multi-damage in a steel cylinder is demonstrated. The results highlight an expanded sensing coverage of the sparse sensor network and its enhanced capacity of acquiring rich information, avoiding the cost of augmenting the number of sensors in a sensor network.

Guide waves-based multi-damage identification using a local probability-based diagnostic imaging method

Multi-damage identification is an important and challenging task in the research of guide waves-based structural health monitoring. In this paper, a multi-damage identification method is presented using a guide waves-based local probability-based diagnostic imaging (PDI) method. The method includes a path damage judgment stage, a multi-damage judgment stage and a multi-damage imaging stage. First, damage imaging was performed by partition. The damage imaging regions are divided into beside damage signal paths. The difference in guide waves propagation characteristics between cross and beside damage paths is proposed by theoretical analysis of the guide wave signal feature. The time-of-flight difference of paths is used as a factor to distinguish between cross and beside damage paths. Then, a global PDI method (damage identification using all paths in the sensor network) is performed using the beside damage path network. If the global PDI damage zone crosses the beside damage path, it means that the discrete multi-damage model (such as a group of holes or cracks) has been misjudged as a continuum single-damage model (such as a single hole or crack) by the global PDI method. Subsequently, damage imaging regions are separated by beside damage path and local PDI (damage identification using paths in the damage imaging regions) is performed in each damage imaging region. Finally, multi-damage identification results are obtained by superimposing the local damage imaging results and the marked cross damage paths. The method is employed to inspect the multi-damage in an aluminum plate with a surface-mounted piezoelectric ceramic sensors network. The results show that the guide waves-based multi-damage identification method is capable of visualizing the presence, quantity and location of structural damage.

A novel probability-based diagnostic imaging with weight compensation for damage localization using guided waves

To avoid direct interpretation of the complicated ultrasonic guided wave signal and the effect of dispersion, probability-based diagnostic imaging is proposed to identify damages for guided wave–based structural health monitoring. However, the probability-based diagnostic imaging algorithm usually requires a relatively large number of actuator–sensor paths to cover the monitoring area. Therefore, a weight-compensated probability-based diagnostic imaging approach is developed to improve the ability of damage localization with higher precision under the same sensor configuration. The weight-compensated probability-based diagnostic imaging algorithm compensates for the weight difference from sensing paths with the unit weight of various positions, inhibited by the path weight difference effect on diagnostic image. The validity of the approach is assessed by identifying damages at different locations with different groups of sensing paths on a stiffened composite panel. Accurate localization results have demonstrated the effectiveness of the developed probability-based diagnostic imaging approach.

Validation and evaluation of damage identification using probability-based diagnostic imaging on a stiffened composite panel

Probability-based diagnostic imaging, as one of the damage identification methods using ultrasonic guided waves, has been attracting increasing attention by researchers in the community of structural health monitoring. However, the probability-based diagnostic imaging algorithm’s influencing parameters, including the selection of certain damage index and frequency, the network of sensing paths, and the size of the effective elliptical distribution area, are empirically determined. This experience dependency limits the application of the method to identify damages in real-world practices. Therefore, it is important to clarify the influences of the above-mentioned various factors on the damage identification. However, the complexity of these factors makes it almost impossible to interpret the influencing mechanisms directly. Thus, a fusion image approach of multiple frequencies is employed to eliminate the influence of different frequencies, while a histogram-based method is proposed to evaluate the reliability of the fusion result. Meanwhile, a unit weight distribution function, considering both the network of sensing paths and the size of the effective elliptical distribution area, is presented in the analysis. Then, the influencing mechanisms are studied and discussed in detail, and a methodology is proposed to optimize the network and the scaling parameter which controls the affected zone.

Evaluation of fatigue cracks using nonlinearities of acousto-ultrasonic waves acquired by an active sensor network

There has been increasing interest in using the nonlinear features of acousto-ultrasonic (AU) waves to detect damage onset (e.g., micro-fatigue cracks) due to their high sensitivity to damage with small dimensions. However, most existing approaches are able to infer the existence of fatigue damage qualitatively, but fail to further ascertain its location and severity. A damage characterization approach, in conjunction with the use of an active piezoelectric sensor network, was established, capable of evaluating fatigue cracks in a quantitative manner (including the co-presence of multiple fatigue cracks, and their individual locations and severities). Fundamental investigations, using both experiment and enhanced finite element analysis dedicated to the simulation of nonlinear AU waves, were carried out to link the accumulation of nonlinearities extracted from high-order AU waves to the characteristic parameters of a fatigue crack. A probability-based diagnostic imaging algorithm was developed, facilitating an intuitive presentation of identification results in images. The approach was verified experimentally by evaluating multi-fatigue cracks near rivet holes of a fatigued aluminum plate, showing satisfactory precision in characterizing real, barely visible fatigue cracks. Compared with existing methods, this approach innovatively (i) uses permanently integrated active sensor networks, conducive to automatic and online health monitoring; (ii) characterizes fatigue cracks at a quantitative level; (iii) allows detection of multiple fatigue cracks; and (iv) visualizes identification results in intuitive images.

Probability-based diagnostic imaging using hybrid features extracted from ultrasonic Lambwave signals

The imaging technique based on guided waves has been a research focus in the field ofdamage detection over the years, aimed at intuitively highlighting structural damage intwo- or three-dimensional images. The accuracy and efficiency of this techniquesubstantially rely on the means of defining the field values at image pixels. In this study, anovel probability-based diagnostic imaging (PDI) approach was developed. Hybrid signalfeatures (including temporal information, intensity of signal energy and signal correlation)were extracted from ultrasonic Lamb wave signals and integrated to retrofit the traditionalway of defining field values. To acquire hybrid signal features, an active sensornetwork in line with pulse–echo and pitch–catch configurations was designed,supplemented with a novel concept of ‘virtual sensing’. A hybrid image fusionscheme was developed to enhance the tolerance of the approach to measurementnoise/uncertainties and erroneous perceptions from individual sensors. As applications, theapproach was employed to identify representative damage scenarios including L-shapethrough-thickness crack (orientation-specific damage), polygonal damage (multi-edgedamage) and multi-damage in structural plates. Results have corroborated that thedeveloped PDI approach based on the use of hybrid signal features is capable ofvisualizing structural damage quantitatively, regardless of damage shape andnumber, by highlighting its individual edges in an easily interpretable binary image.