Lamb Wave-Minimum Sampling Variance Particle Filter-Based Fatigue Crack Prognosis

Gao, Peiwei

doi:10.3390/s19051070

Cited by 8 publications

(5 citation statements)

References 40 publications

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“…10,11 Therefore, the improvement of the resampling and the importance sampling method in particle filtering algorithm has become a research hotspot for researchers. Studies presented in Refs 12,13 are similar to the ones shown in this paper.…”

Section: Introductionsupporting

confidence: 87%

Prediction of fatigue crack propagation based on dynamic Bayesian network

Wang

Yang

et al. 2022

Advances in Mechanical Engineering

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To address the problem of low prediction accuracy in the current research on fatigue crack propagation prediction, a prediction method of fatigue crack propagation based on a dynamic Bayesian network is proposed in this paper. The Paris Law of crack propagation and the extended finite element method (XFEM) are combined to establish the state equation of crack propagation. The uncertain factors of crack propagation are analyzed, and the prediction model of fatigue crack propagation based on the dynamic Bayesian network is constructed. A Bayesian inference algorithm based on the combination of Gaussian particle filter and firefly algorithm is proposed. The fatigue experiment of the specimen with the pre-cracks is carried out to test the correlation between the fatigue load cycles and the crack propagation depth. The experimental results show that the crack propagation prediction method proposed in this paper can effectively improve the prediction accuracy of crack propagation depth.

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Section: Introductionsupporting

confidence: 87%

Prediction of fatigue crack propagation based on dynamic Bayesian network

Wang

Yang

et al. 2022

Advances in Mechanical Engineering

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“…The state-space model based on the crack propagation law consists of a state equation and observation equation [28], as shown in Eq. (1).…”

Section: Framework Of the Particle Filter Based On Fatigue Crack Grow...mentioning

confidence: 99%

Aluminum Alloy Fatigue Crack Damage Prediction Based on Lamb Wave-Systematic Resampling Particle Filter Method

Zhao¹,

Liu²,

Sun³

et al. 2022

Structural Durability &Amp; Health Monitoring

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Fatigue crack prediction is a critical aspect of prognostics and health management research. The particle filter algorithm based on Lamb wave is a potential tool to solve the nonlinear and non-Gaussian problems on fatigue growth, and it is widely used to predict the state of fatigue crack. This paper proposes a method of lamb wavebased early fatigue microcrack prediction with the aid of particle filters. With this method, which the changes in signal characteristics under different fatigue crack lengths are analyzed, and the state-and observation-equations of crack extension are established. Furthermore, an experiment is conducted to verify the feasibility of the proposed method. The Root Mean Square Error (RMSE) of the three different resampling methods are compared. The results show the system resampling method has the highest prediction accuracy. Furthermore, the factors affected by the accuracy of the prediction are discussed.

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“…However, the optimal number of particles in the PF algorithm is often difficult to choose. In addition, too many particles will reduce the computational efficiency and affect the real-time prediction of crack growth (Yang and Gao, 2019). By introducing intelligent optimization algorithms into the standard PF algorithm, the particles obtained by a priori sampling can move to the region with high a posteriori probability, and the problem of particle leanness can also be overcome.…”

Section: Introductionmentioning

confidence: 99%

Prediction of fatigue crack propagation in metals based on IBAS-PF

Wensheng

Zhenhua

Jiefei

et al. 2022

Front. Built Environ.

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Owing to particle leanness, the standard Particle Filter (PF) algorithm is prone to the problem of reduced prediction accuracy when predicting fatigue crack propagation. An improved particle filter algorithm based on the optimization algorithm of beetle antenna search (IBAS-PF) for fatigue crack propagation in metals is proposed in this paper. The discrete Paris formula was used to establish the state equation of fatigue crack propagation, in which the uncertainty of material and crack propagation process were considered. Meanwhile, the characteristics of Lamb wave signals under different crack lengths were extracted to establish the observation equation. The sampling process of the PF algorithm was optimized based on the beetle antennae search algorithm to improve the particle diversity and the prediction accuracy. Compared with the standard PF algorithm, the improved BASO-PF algorithm has higher accuracy for metal fatigue crack propagation, as well as better state estimation ability.

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Lamb Wave-Minimum Sampling Variance Particle Filter-Based Fatigue Crack Prognosis

Cited by 8 publications

References 40 publications

Prediction of fatigue crack propagation based on dynamic Bayesian network

Prediction of fatigue crack propagation based on dynamic Bayesian network

Aluminum Alloy Fatigue Crack Damage Prediction Based on Lamb Wave-Systematic Resampling Particle Filter Method

Prediction of fatigue crack propagation in metals based on IBAS-PF

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