Structural Damage Detection Based On Image Processing Technology And Wavelet Method

In recent a few decades, the structural health monitoring (SHM) and damage detection based on vibration receive more attention among the scholars. The digital image processing technology has the features such as long distance, noncontact and high accuracy. It can achieve the real time and online monitoring in SHM. Wavelet analysis is a time-scale (time-frequency) analysis of signal. It is a multi-resolution method, and has the ability to detect the local features of the signal in time and frequency domain. There are more and more utilization in damage detection by using wavelet. Combination of digital image processing and wavelet analysis provides a new method for signal measurement of vibrational response and damage detection. With the fund of the national natural science foundation of China (50778077,50925828), the structural damage detection based on digital image processing and wavelet analysis is studied.In this dissertation, following aspects have been studied theoretically, numerically and experimentally. Some important results and conclusions have been acquired:(1) The selection of mother wavelet in damage detection is analyzed. The symmetrical wavelet is suitable for damage detection. When the order of vanishing moments is higher, the support length is longer and the damage detection result is more distinct, but more computational time is needed. Thus we should find the balance between the number of vanishing moments (NVM) and computational time. The NVM should be more than two at least, and the fourth vanishing moments should be very appropriate.(2) The damage detection method is studied based on the statistical moments of the energy density function of the vibration responses in the time-scale domain. The continuous wavelet transform is conducted to decompose the vibrational responses into discrete energy distributions. The damage indices relative zeroth moments and normalized zeroth moments are established by the wavelet coefficients. The damage threshold is determined by the statistical hypothesis test. The sensitivity matrix of element modal strain energy change is derived to quantify the damage using a few numbers of lower modes. Simulation results of a plate show that the proposed approach is able to identify the single and multiple damage cases with artificial noises.(3) The free vibration responses of structure are transformed by the wavelet, and the damage detection index is established based on the residual wavelet forces. The natural excitation is introduced to be a stationary random process, random decedent technique is used to simulate the free vibration responses. Combined with the matrix disassembly technique the damage is identified in the plane truss. The method is able to detect the location and extent of damage and is insensitive to the noises.(4) The difficulties of traditional multi-dynamic displacement measurement are massive data and synchronization. In order to solve these problems, multi-point dynamic displacement-measurement system is developed based on digital image processing technology. The displacement accuracy can reach 0.01 mm by the wavelet subpixel edge detection method. The disk arrays and synchronization signal generator ensure massive data processing and multi-channel synchronization between cameras. The four-channel dynamic-displacement acquisition is carried out on a four-story steel frame structure. The results are compared with the displacement sensors. The system is able to provide the accuracy displacement data for structural health monitoring and damage detection.(5) A method based on digital image processing and wavelet transform is presented for structural damage detection. The displacement time series of the beam at sub-pixel resolution are analyzed to obtain mode shape by high speed cameras. Then curvature mode shape is calculated by the mode shape. In single and multiple damage scenarios, curvature mode shapes and wavelet-transformed curvature mode shapes both can detect the damage location. The Lipschitz exponents are calculated by wavelet coefficients to identify the damage extents. Experimental results of a simple-supported beam show that the proposed approach is able to identify the locations and extents of the single and multiple damage scenarios.