Application Of Particle Filter In Structural Damage Identification

During the period of structural service,long-term effects of natural environments and human factors will lead structures to damage,thus causing serious safety accidents.Structural damage identification is the core technology of structural health monitoring(SHM)and safety assessment.The research of structural damage identification methods can guarantee the safety,integrity and durability of the structure and prevent the occurrence of disasters.Particle filter(PF)is a recursive statistical filter based on the Monte Carlo method and Bayesian estimation.PF can estimate the state of nonlinear systems using measurement signals with gaussian or non-gaussian noise.Therefore,this method has been widely applied in various fields.This paper systematically analyzed particle filter in terms of structural damage identification.In order to solve the problems of particles degradation and the illposedness of inverse problem,two improved particle filter methods are proposed.Numerical simulations and vibration experiments of the frame structure verified their effectiveness.The main content of this article is as follows:(1)Based on particle swarm optimization(PSO)algorithm,an improved particle filter method is proposed to solve the problems of particles degeneracy,ill-posed characteristics and so on.These problems are common when the particle filter is applied to identify structural damages.The process of PSO is used to push particles to move toward the regions with higher posterior probability density,so the importance sampling process of particle filter is optimized.Furthermore,according to the sparseness of structural damage parameters distribution,the zero-mutation operation of damage parameters in particles is introduced to maintain the diversity of particles and improve the ill-posedness of inverse problem.Numerical simulations and shaking table test of frame structure show that the proposed method in this article can accurately identify the position and degree of structural damage.(2)Particle filter has difficulties in selecting the importance density function(IDF)and particles degradation.Thus,this paper uses UKF to obtain the IDF,that can introduce the latest measurement information into the sequential importance sampling(SIS)process.Due to the sparseness of structural damage parameters distribution,an improved unscented particle filter(UPF)method with L1-norm regularization algorithm was proposed.This method improves particles diversity,the ill-posedness of inverse problem and anti-noise performance.(3)An experimental model of frame structure is used to verify the effectiveness of the proposed method in this article.The structural dynamic response signals under different damage conditions are obtained through sensors on each layer.The experimental results show that the proposed algorithm can accurately estimate the structural motion state and accurately identify the structure damage location and damage degree.