Telecharger [UPD] Crack Wave Xtractor
1D CNNs and RNNs are two popular structures to recognize patterns from 1D signals. Abdeljaber et al. trained multiple 1D CNNs to detect whether damage exists at specific locations (joints)18. Their model uses the acceleration signal at each joint as input and requires extra workload to segment the signal into frames. Considering damage detection as a binary prediction problem, i.e., predicting whether a crack exists from input data, 1D CNN, RNN, and LSTM models all can achieve high accuracy19. In one following paper, the authors developed a two-stage damage detection method20. The method determines whether a sample is damaged or not at the first stage and then predict the location and length of the damage with another regressor network. However, the regressor network deals only with the damage that is orthogonal to the sample's surface. By coupling the vibration response of undamaged and fully damaged structures, the authors can train for each possible damage location in a 1D-CNN model with much less data21. Lin and his colleagues investigated damage detection using low-level waveform signals for a simply supported beam22. They segmented the simulated time-series signals and added noise to augment the dataset. The 6 stacked 1D-ConvLayers were used as feature extractor. The model achieves a high accuracy for single and multiple damage, and is robust to noise. Rai and Mitra proposed a multi-headed 1D-CNN architecture for damage detection based on raw discrete time-domain Lamb wave signals recorded from a thin metallic plate23. Both simulated data and experimentally generated data are used to train and evaluate the model. The model performs well to detect notch-like damage on both datasets. Besides this method, there is also an attempt to use unsupervised methods to identify undefined damages based on the features that are extracted by CNNs24.
Telecharger Crack Wave Xtractor
Training the deep learning models requires a large amount of data. For wave-based crack detection models, these necessary data can be generated from numeric simulations such as Finite Element Method (FEM)s, or from the sensory measurements of a lab/field configuration5. The numerical treatments for the paper are done by use of a meso-scale method as it is better suited to capture the effects as initial cracking and crack propagation depending on the material parameter and the initial- \& boundary conditions without pre-definition of damaged pixels, and it is also applicable for 2D and 3D problems. The Lattice Element Method is a class of discrete models in which the structural solid is represented as a 3D assembly of one-dimensional elements25,26,27. This idea allows one to provide robust models for propagation of discontinuities, multiple crack interactions, or cracks coalescence even under dynamic loads and wave fields. Different computational procedures for lattice element methods for representing linear elastic continuum have been developed. Beside different mechanical, hydro-mechanical and multi-physical developments, the extension and basics for a new dynamic Lattice Element Method was proposed as well28,29. This development will be used in the given paper for the health monitoring of structures.
The assembly of the heterogeneous and homogeneous material will be generated by specific meshing algorithms in LEM. The Lattice Element Models with the lattice nodes can be considered as the centers of the unit cells, which are connected by beams that can carry normal force, shear force and bending moment. Because the strain energy stored in each element can be exceeded by a given threshold, the element is either removed for cracking or assigned a lower stiffness value. The method is based on minimizing the stored energy of the system. The size of the localized fracture process zone around the static or propagating crack plays a key role in failure mechanism, which is observed in various models of linear elastic fracture mechanics and multi-scale theories or homogenization techniques. Normally this propagating crack process needs a regularization, however, an efficient way of dealing with this kind of numerical problem is by introducing the embedded strong discontinuity into lattice elements, resulting in mesh-independent computations of failure response. The generation of the lattice elements are done by Voronoi cells and Delaunay itself27,30. With the performance of this procedure an easy algebraic equation is generated for the static case. To develop the dynamic LEM for simulation of a propagating wave field, a more complex extension of the LEM is needed. The following solution of the dynamic LEM is solved as a transient solution in the time domain.
The basic idea of developing deep learning models for damage detection in the given case of propagating wave fields is the identification of wave field patterns respective of the change in wave field patterns during the damage evolution. The damage evolution process covers the initial static case on the given plate. After a change of surrounding, static stress condition damages on different positions in the plate area can be created depending on the stress condition and the material parameter. Before and after a damage scenario, a small strain wave field is excited to propagate through the plate. Because of the damage/crack, within the plate the pattern of the propagating wave field will be modified. The interaction of the wave field within the crack is essential for identifying the correct wave field. Under the assumption of an open crack, neglecting shear slipping and crack growth under dynamic loads, the crack will produce a mode conversion and a scattering of the propagating wave field28,29. It becomes obvious that the transient solution provides that phenomena.
Conceptual explanation of the proposed crack detection model with 1D-CNN detector. (A) Diagram of a 1D-CNN that transforms a discretized wave input and produces a discrete feature sequence. (B) Internal STRUCTURE of a 1D-CNN layer, consisting of a trainable weight \(W^\left(h\right)\), a bias \(b^\left(h\right)\). Activation function is represented by \(\sigma\). C. Diagram of the complete structure of the proposed model.
Figure 7 clearly shows the arrival time of the wave fields at each reference point. The closest reference point (\(R_2\)) has the maximum amplitude and minimum arrival time. In Fig. 7 a, the arrival time of the wave field to \(R_1\approx R_3\), \(R_4\approx R_6\) and \(R_7\approx R_9\). Due to the generated discontinuity (crack) in Fig. 7 b, the first arrival times of the wave field to \(R_3\), \(R_6\) and \(R_9\) are delayed. Theses reference points are located in the shadow field behind the generated discontinuity. Having a closer look at the \(R_2\), it is obvious that due to the wave reflection from the generated discontinuity, the arrival of the second wave field happens sooner than the first boundary condition, approximately \(1.45\times 10^-6s\). The length, location and orientation of the discontinuities affect the wave field in the domain. The simulated wave fields at the reference points are used for training and developing the artificial neural network model.
In total, we generated 3040 samples for training and 320 samples for testing. There are different types of samples with respect to the randomness of sample generation and crack generation. The plate and crack of Type-N samples are both generated randomly. The reference samples without any crack inside are marked as Type-R. The samples of different plates with similar cracks (Type-S) and the same plates with different cracks (Type-C) are also generated. Fig. S4 shows a detailed distribution of these 4 types in test dataset. Among all test samples, we intentionally generated 7 random samples, and each one has its counter case in the training dataset in terms of the same crack (as well as no-crack cases). It is worth emphasizing that these samples are not repeated ones. Because of the randomness in the generation process, the diversity of the interior particles and their wave field patterns is ensured.
The paper presents a new approach to detect damages by wave pattern recognition models. The major development is a learning CNN to detect on-hand the visible wave pattern of the damaged zone within a solid structure. To generate the cracked structure, a new dynamic Lattice Element method was used. The major advantage of this method is the application to heterogeneous structures under mechanical, hydraulically, thermal field influence and local chemical changes to describe the evolution of damages in solid structures. The use of new generation deep CNNs to analyse the time dependency within the changed wave pattern is promising. With the described method, a stable detection of 90 percent of the generated large cracks was possible. The next steps will be the reduction of the used number of receivers and increasing the model's ability of tiny crack detection.
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Lack of fusion is the poor adhesion of the weld bead to the base metal; incomplete penetration is a weld bead that does not start at the root of the weld groove. Incomplete penetration forms channels and crevices in the root of the weld which can cause serious issues in pipes because corrosive substances can settle in these areas. These types of defects occur when the welding procedures are not adhered to; possible causes include the current setting, arc length, electrode angle, and electrode manipulation. Defects can be varied and classified as critical or non critical. Porosity (bubbles) in the weld are usually acceptable to a certain degree. Slag inclusions, undercut, and cracks are usually unacceptable. Some porosity, cracks, and slag inclusions are visible and may not need further inspection to require their removal. Small defects such as these can be verified by Liquid Penetrant Testing (Dye check). Slag inclusions and cracks just below the surface can be discovered by Magnetic Particle Inspection. Deeper defects can be detected using the Radiographic (X-rays) and/or Ultrasound (sound waves) testing techniques.