Damage detection of three-dimensional braided composite materials using carbon nanotube thread

In this study, a reference-free damage characterization technique is developed not only to identify but also to locate and quantify damage in composite structures subject to varying temperature conditions. First, damage is characterized in terms of a damage index (m-value) defined as the ratio of damage size to the wavelength of the A0 mode within the damage. Then, a feasible solution space defining all possible combinations of the damage location and size are estimated without using any prior baseline data obtained from the pristine condition of a structure or different paths. When additional information such as the A0 mode group velocity within the pristine region of the structure becomes available, the estimates for the damage location and size are updated with better accuracy. The uniqueness of this study lies in that damage localization and quantification as well as identification are all performed without comparing current Lamb wave signals with the ones obtained from the pristine condition of the target structure, making the proposed technique more attractive for online monitoring. Numerical and experimental tests are presented to demonstrate the effectiveness of the proposed damage detection technique under varying temperature.

ABSTRACTStructural Health Monitoring (SHM) takes advantage of the recent advances in nanotechnology and sensing in order to monitor the behavior of a structure, assess its performance and identify damage at an early stage. Monitoring the state of strain throughout an entire structure is essential to determine its state of stress, detect potential residual stresses after fabrication, and also to help to establish its integrity. The Carbon nanotube thread was integrated into three-dimensional braiding materials and used for the first time as a sensor to monitor strain and also to detect damage in the three-dimensional braided composite material.In this paper a literature review about the application of carbon nanotubes thread for sensors and smart materials used for SHM of braiding structures is presented. The test data show the braided angle is important parameter for structural health monitoring of three-dimensional. The research will provide a new integrated and distributed technologies for the built-in carbon nanotube sensor to detect the health of composite. The subject will provide the new idea and method for the development of smart composite materials research and application.

Summary Anew damage identificationmethodbasedonFractal theoryandwaveletpacket transform is presented in the paper. The damage identification method utilize frequency-based domain message, time-based domain message and space based domain message effectively. NeXT method were first employed to reduce loading effect of random vibration measured from structures and then the vibration sinal were decomposed into the certain level wavelet packet components. For every time array of wavelet packet component then need to calculate the mean FD values as the ultimated FD values to reduce noiseeffect. The location of damage in the beam can be determined by the dramatic fluctuation appearing on contour of estimated FD and the extent of the damage can be estimated by FD-based damage index of δ. As a validation, the proposed method is applied to detect damage in a simply supported beam by numerical study. The successful detection of the damage in the beam demonstrates that the method is capable of assessing both the location and size of the damage. Noise stress testsare also carried out to demonstrate the robustness of the method under the influence of noise by considering all the information of every wavelet packet component synthetically.

Fiber-optic sensors cannot measure damage; to get information about damage from strain measurements, additional strategies are needed, and several alternatives are available in the existing literature. This paper discusses two independent procedures. The first is based on detecting new strains appearing around a damage spot. The structure does not need to be under loads, the technique is very robust, and damage detectability is high, but it requires sensors to be located very close to the damage, so it is a local technique. The second approach offers wider coverage of the structure; it is based on identifying the changes caused by damage on the strain field in the whole structure for similar external loads. Damage location does not need to be known a priori, and detectability is dependent upon the sensor’s network density, the damage size, and the external loads. Examples of application to real structures are given.

Based on the amplitude attribute analysis of ground penetrating radar (GPR) data, this paper applies GPR to the identification of internal damages and abnormal objects in stone walls of Tibetan ruins. The corresponding relationship between the characteristics of internal damages and abnormal objects and the radar data is explored through the Tibetan stone walls (TSWs) simulation test. The identification of location and size of the damages and abnormal objects in TSWs is realized based on the Mahalanobis distance abnormal data discrimination method. Using the normal distribution and the noncentral chi-square distribution, the identification law of types of the damages and abnormal objects is constructed, which takes root mean square (RMS) amplitude and interface reflection coefficient as the characteristic values. The field detection results of TSWs at three Tibetan sites are taken as an extension and supplement to the simulation results, and a set of identification atlas for the location, size and type of the internal damages and abnormal objects in TSWs is established. The application results of the identification atlas were verified by the field detection and the results show that the atlas has a high accuracy, which can significantly improve the efficiency of the identification, and can provide a basis for the performance research, protection and maintenance of TSWs.

Fukunaga-Koontz feature transformation for statistical structural damage detection and hierarchical neuro-fuzzy damage localisation

Transmissibility of non-linear output frequency response functions with application in detection and location of damage in MDOF structural systems

Nondestructive evaluation (NDE) for detection and quantification of damage in composite materials is fundamental in the assessment of the overall structural integrity of modern aerospace systems. Conventional NDE systems have been extensively used to detect the location and size of damages by propagating ultrasonic waves normal to the surface. However they usually require physical contact with the structure and are time consuming and labor intensive. An automated, contactless laser ultrasonic imaging system for barely visible impact damage (BVID) detection in advanced composite structures has been developed to overcome these limitations. Lamb waves are generated by a Q-switched Nd:YAG laser, raster scanned by a set of galvano-mirrors over the damaged area. The out-of-plane vibrations are measured through a laser Doppler Vibrometer (LDV) that is stationary at a point on the corner of the grid. The ultrasonic wave field of the scanned area is reconstructed in polar coordinates and analyzed for high resolution characterization of impact damage in the composite honeycomb panel. Two methodologies are used for ultrasonic wave-field analysis: scattered wave field analysis (SWA) and standing wave energy analysis (SWEA) in the frequency domain. The SWA is employed for processing the wave field and estimate spatially dependent wavenumber values, related to discontinuities in the structural domain. The SWEA algorithm extracts standing waves trapped within damaged areas and, by studying the spectrum of the standing wave field, returns high fidelity damage imaging. While the SWA can be used to locate the impact damage in the honeycomb panel, the SWEA produces damage images in good agreement with X-ray computed tomographic (X-ray CT) scans. The results obtained prove that the laser-based nondestructive system is an effective alternative to overcome limitations of conventional NDI technologies.

Baseline-free assisted lamb wave-based damage detection in CFRP composites using graph convolutional networks and Transformer models

Damage detection for beam-like structures using the normalized curvature of a uniform load surface

This paper presents a brief overview of vibration-based damage identification studies based on Deep Learning (DL) in civil engineering structures. The presence, type, size, and propagation of structural damage on civil infrastructure have always been a topic of research. In the last couple of decades, there has been a significant shift in the damage detection paradigm when the advancements in sensing and computing technologies met with the ever-expanding use of artificial neural network algorithms. Machine-Learning (ML) tools enabled researchers to implement more feasible and faster tools in damage detection applications. When an artificial neural network has more than three layers, it is typically considered as a ?deep? learning network. Being an important accomplishment of the ML era, DL tools enable complex systems which are made of several layers to learn implementations of data with outstanding categorization and compartmentalization capability. In fact, with proper training, a DL tool can operate directly with the unprocessed raw data and help the algorithm produce output data. Competitive capabilities like this led DL algorithms perform very well in complicated problems by dividing a relatively large problem into much smaller and more manageable portions. Specifically for damage identification and localization on civil infrastructure, Convolutional Neural Networks (CNNs) and Unsupervised Pretrained Networks (UPNs) are the known DL tools published in the literature. This paper presents an overview of these studies.

Impact of time synchronization error on the mode-shape identification and damage detection/localization in WSNs for structural health monitoring

Hybrid approach for two dimensional damage localization using piezoelectric smart aggregates

Response surface methodology for damage detection using frequency and mode shape

Damage nucleation and growth can be complex in hybrid bonded structures composed of metals and laminated composites. There are limited reliable analytical and empirical methods to evaluate the bond integrity of such structures and to quantify the state of bonding in hybrid joints. Depending on the geometry and accessibility to hybrid joint sections, ultrasonic Nondestructive Testing (NDT) techniques are available for inspection of these structures. The interfacial damages at the bondline of structural joints represent challenging detection tasks for traditional ultrasonic nondestructive inspection (NDI) methods such as the pulse–echo method. This work investigates a method for bonded joint inspection that uses interface guided ultrasonic waves propagating along the bondline of these hybrid joints. An analysis, based on the use of finite element models for hybrid structures, is conducted to examine the use of interface waves to detect common defects found at or in the vicinity of interfaces of hybrid isotropic-anisotropic structures. Both cohesive and adhesive damage in a bonded joint are examined. Several damage scenarios are numerically studied and the guided wave time of flight (TOF) is shown to be sensitive to the size and location of interfacial damages. The results of this study suggest that interface guided waves can be used for integrity assessment and damage detection in hybrid structures.