Beam Structure Damage Detection Based On Improved PSO-Kalman Algorithm

Structural health monitoring has gradually become a hot spot in the field of civil engineering.Recognition of structural damage has attracted more and more researchers' attention.In recent years,with the development of science and technology and economy,a variety of structural buildings have emerged in the era.In recent years,the collapse of buildings has brought about irreparable consequences and losses.The main reason is that it has not been able to find out in the damage accumulation phase and it is difficult to save the structure before the accident.In order to ensure the safety and durability of the structure,it is necessary to perform damage detection on the existing structure.The structural damage indicators currently used for damage detection can be constructed in many ways.The most frequently used ones are constructed using the information of modal parameters.However,it cannot be denied that many kinds of experiments have confirmed that the indicators constructed by modal parameters cannot Neglecting the influence of noise,the recognition result will be interfered with by various noises in different degrees,which will lead to an accurate judgment of the structural damage unit.This is also the research bottleneck of structural damage recognition.In order to solve the problem of noise interference in the measured signal,this paper selects the modified unit damage variable as the damage identification index,and optimizes the structure damage detection signal through a combination of particle swarm optimization and Kalman filter algorithm to avoid signal maximization.The excess noise collected in the collection device and the signal transmission instrument filters out unwanted interferences during the acquisition and transmission process.Through Matlab software,it is difficult for the Kalman algorithm to obtain the statistical characteristics of the noise,combined with the fitness function in the PSO to accurately find the corresponding filter parameters.The analysis results of Kalman's conventional simulation test function show that the improved particle swarm Kalman algorithm performs better than the standard Kalman algorithm.Focused on the selection of fitness function,many improved denoising methods and the denoising effect of literature are directly compared with the known pure signal.That is,the validation effectiveness is only for the simulated signal of the test,and all the simulations are defaulted.Although the pure signal is known,although the evaluation effect index has been greatly improved,it has little feasibility for application to practical projects.Therefore,in order to be applicable to the measured vibration signal,this paper proposes a specific solution approach,using the finite element model to simulate the dynamic response of the structure as a pure signal,adding white noise as a noisy signal in the pure signal,so that the noisy signal and the measured signal The same signal to noise ratio.The signal-to-noise ratio of the measured signal can be estimated based on the eigenvalue decomposition of the covariance matrix.The mean square error of pure signal and noisy signal is used as the fitness function.The particle swarm Kalman filter is used to obtain the optimal value of the noise matrix.The particle swarm Kalman algorithm can be successfully applied to the denoising of the measured signal.Then we can use existing theory to extract the complete modal information by random subspace method and static agglomerative method to carry out more accurate identification of the unit damage variables.The vibration test of reinforced concrete simply supported beam with rectangular cross-section and I-shaped steel beam was carried out to obtain the vibration acceleration signal before and after the structural damage.Firstly,the improved particle swarm Kalman algorithm is used to filter the signal.Then the first three-order modal parameters of the structure are extracted from the measured signal by the stochastic subspace method,and an improved damage identification index is constructed for positioning.Compared with the results of wavelet soft thresholding and D-S fusion processing,the results further show that this method is applicable to the feasibility and superiority of preprocessing of structural damage identification signals.