S. A. Ravanfar

An Improved Method of Parameter Identification and Damage Detection in Beam Structures under Flexural Vibration Using Wavelet Multi-Resolution Analysis

This paper reports on a two-step approach for optimally determining the location and severity of damage in beam structures under flexural vibration. The first step focuses on damage location detection. This is done by defining the damage index called relative wavelet packet entropy (RWPE). The damage severities of the model in terms of loss of stiffness are assessed in the second step using the inverse solution of equations of motion of a structural system in the wavelet domain. For this purpose, the connection coefficient of the scaling function to convert the equations of motion in the time domain into the wavelet domain is applied. Subsequently, the dominant components based on the relative energies of the wavelet packet transform (WPT) components of the acceleration responses are defined. To obtain the best estimation of the stiffness parameters of the model, the least squares error minimization is used iteratively over the dominant components. Then, the severity of the damage is evaluated by comparing the stiffness parameters of the identified model before and after the occurrence of damage. The numerical and experimental results demonstrate that the proposed method is robust and effective for the determination of damage location and accurate estimation of the loss in stiffness due to damage.

A Hybrid Procedure for Structural Damage Identification in Beam-Like Structures Using Wavelet Analysis

There are significant changes in the dynamic responses of damaged structures in which the damage depth becomes influential. This fact enables the recognition of damages in structures from their dynamic response data. This paper aims to identify damage locations as well as quantification of damage extent utilizing a vibration-based method. In this work, a signal-based damage identification procedure using the information entropy of wavelet packet energy is employed to evaluate beam-like structures. Damage indices called singular wavelet packet entropy and relative wavelet packet entropy are proposed to identify the location and to quantify the structural damage. The method involves two steps: firstly, the singular value of wavelet packet entropy, which reveals the abnormality distribution of the energy in signal time-frequency domain and secondly, relative wavelet packet entropy that is an acceptable damage feature to examine damage location and quantitative evaluation of the extent of damages through the vibration signals. The effects of wavelet type and decomposition level on the detection of damage location are examined. Numerical and experimental results from four test beams are used to verify the viability of this method. Results show that the damage indices method has great potential in identification of location and depth of cut in a steel beam. The procedure can be used for health monitoring of other complex structures.

Damage Detection Based on Wavelet Packet Transform and Information Entropy

In this study, a new approach for damage detection in beam-like structures is presented. Damage feature such as relative wavelet packet entropy (RWPE) based on the decomposed component is used to identify the damage location and evaluate the damage severity. RWPE describes present information of relative wavelet energies correlated with different frequency ranges. It is should be noted that the acceleration measured in the all three directions should be used in computation of RWPE. Numerical and experimental results are used to verify the practicality of this method. Results show that the damage index technique has great potential in detection of location and depth of cut in a steel beam. The procedure can be applied for health monitoring of other complex structures.

Damage Detection Optimization Using Wavelet Multiresolution Analysis and Genetic Algorithm

In this study, an optimized damage detection using genetic algorithm (GA) in beam-like structures without using baseline data. For this purpose, a vibration-based damage detection algorithm using a damage indicator called ‘Relative Wavelet Packet Entropy’ (RWPE) was applied to determine the location and severity of damage. To improve the algorithm, GA was utilized to optimize the algorithm so as to identify the best choice of wavelet parameters. To examine the robustness and accuracy of the proposed method, numerical examples and experimental cases with different damage depths were considered and conducted.

Ensemble neural networks for structural damage identification using modal data

Damage in a structure is defined as changes to its geometric and material properties, leading to a reduction in the structural stiffness which negatively affects the performance of the structure. Reduction in the structural stiffness produces changes in the modal parameters such as the natural frequencies and mode shapes. Artificial neural networks (ANNs) have been applied extensively in recent years due to their excellent pattern recognition ability that is useful for structural damage identification purposes. In this paper, ANNs based damage identification techniques were developed and applied for damage localization and severity identification in I-beam structures using dynamic parameters. Experimental modal analysis and numerical simulations were applied to generate dynamic parameters of the first five flexural modes of structures. In damage identification using ANNs, five individual networks corresponding to mode 1 to mode 5 were trained, and then a method based on neural network ensemble was proposed to combine the outcomes of the individual neural networks into a single network. The ensemble network has the advantages of all the individual networks from different vibrational modes. The results showed that ensemble neural networks have a strong potential for structural damage identification.