Papers by Keyword: Crack Identification

Abstract: Wavelet Transform (WT) and Wavelet Packet Transform (WPT) approaches have shown great promise in the field of signal analysis in recent decades. The main merit of these methods is their capability in localization of each signal in distinct time or space interval. However, the frequency resolution of such transformation is relatively poor in high frequency regions. The WPT, which is an extended form of the WT, provides a complete level-by-level signal decomposition. Therefore, a frequency analysis creates an arbitrary time. In this study, dynamic transient analysis is performed employing a finite element software (ANSYS) on a beam and acceleration time history of various points is investigated. Then, the captured signals are decomposed to the wavelet packet components using MATLAB and energy rate index is calculated for each component utilizing a wavelet packet rate index (WPERI). The results indicate that not only are the obtained index values sensitive, but they also are precise for the crack identification.

Abstract: The paper present a novel non-destructive evaluation method designed to assess damage in structural elements subjected to important axial loads, as columns are. In the prior research a reliable damage detection method was developed for beams subjected to own mass, that consider the relation existing between the energy stored in the beam in certain vibration modes and the related natural frequencies. First we found the mathematical relations expressing the healthy pillar mode shapes and frequencies with respect to the top mass. Afterward, by means of FEM, we derived the natural frequencies for the numerous damage cases, in order to define the frequency shift curves. Analogous to the case of beams, the damage location is characterized by patterns that are derived from the mode shape curvatures square of the healthy beam. The damage location becomes an inverse problem while the damage position is found by interpreting frequency measurements made on the healthy and damaged beam.

Abstract: Problem of identifying building crack is studied to provide technical support for the construction quality. In the building, building crackis an important factor affecting the construction quality. This paper presents a building crack recognition method based on cloud computing. Cloud model is established to pretreat the acquired building image, so as to improve image quality, and perform construction cracks identificationaccording to the processed image. Experimental results show that the improved algorithm for cracks construction identification can improve the recognition accuracy.

Abstract: Problem of identifying building crack is studied to provide technical support for the construction quality. In the building, building crackis an important factor affecting the construction quality. This paper presents a building crack recognition method based on cloud computing. Cloud model is established to pretreat the acquired building image, so as to improve image quality, and perform construction cracks identificationaccording to the processed image. Experimental results show that the improved algorithm for cracks construction identification can improve the recognition accuracy.

Abstract: Focusing on the damage detection using the fractal dimension analysis in plates, this study begins by giving a fractal dimension scanning method for beams. Then extend the algorithm to the plates by degrading the plate mode shape into transverse and longitudinal directions. With the affine transformation, the proposed method overcomes the incapability of identifying damage when using the higher-order mode shapes of the existing fractal dimension methods. Numerical results show that different types of cracks, crack locations and lengths can be detected using this method.

Abstract: Structural modified crack model is deduced to analyze modal parameters of the damaged beam by using of perturbation method. Firstly, the optimal fitting formula of inertia moment at the damage location is proposed to consider damage's influence on structural stiffness and mass matrix. Then, the solutions of the first and second vibration perturbation equations are deduced. Compared with experimental results of simply-supported cracked beams, this method's reliability is proved.

Abstract: Based on EMI and ANN techniques, crack location in coupling beam was studied by the experimental platform which using the WK6500 impedance analyzer. Impedance curves of the different PZT was obtained to diagnosis the situation of coupling beam whether it was perfect or cracked, and then located the crack by neural network technique to if the coupling beam was cracked.

Abstract: Based on the finite element theory, a method is proposed for crack identification of simple beam via the wavelet analysis of vibration modal parameters. A cracked simple beam is simulated using finite element method, and its modal parameters, including first three-order vibration modes, are obtained. Then, these modal parameters are analyzed via mexh and db wavelets. The crack location of the simple beam is identified by the maximum of wavelet coefficients, which validates the proposed method. This research may be useful in crack identification of simple beam structures.

Abstract: Ultrasonic infrared thermography is a novel nondestructive detection technique, which combines a short ultrasonic pulse excitation and infrared imaging to detect defects, such as crack, in materials and structures. A simplified one-dimension heat-conduction model excited by ultrasonic pulses is put forward in this paper. Based on this model, a serial of image processing methods for recognition and reconstruction of cracks were presented. Results obtained show that the proposed method is creditable and applicable.

Abstract: The paper performs an experimental research on the crack identification of drawing parts using AE technique. Under the platform of the AE system, the AE signals of drawing parts crack are acquired. BP neural network is designed with three layers. They are ten neurons of input layer, three neurons of output layer and thirteen neurons of hidden layer. The characteristic parameters of the crack acoustic emission are considered as the input of BP neural network to exercise the network. The test data are inputted to the neural network after it is exercised. The test result is in accord with the experiment result. The method is proper to identify the crack of drawing parts. The emergence of many inferior parts and the waste of resource can be avoided. It also can debase the cost of manufacture and improve the productive efficiency.