Remaining useful life prediction of silicon foam material based on double exponential particle filter model

The traditional physical model has poor interpretability and low prediction accuracy in evaluating remaining useful life of silicon foam materials. This paper presents a remaining useful life prediction method based on double exponential particle filter model. Based on the stress relaxation mechanism of silicon foam material, a more interpretability double exponential stress degradation model was established by selecting the load retention rate of silicon foam structure as the characteristic quantity. Firstly, the least square method was used to fit the observed data for initializing the model parameters and health state. Then, the Bayesian theory was used to track the state of historical samples, update the state transfer function, and realize the degradation trend prediction of load retention rate and remaining useful life assessment. The generalization applicability and accuracy of the double exponential particle filter model for predicting the residual life of silicon foam materials were verified by simulation and experiment. At the same time, the prediction results were compared with those of the traditional exponential model. The results show that the proposed method has better prediction accuracy and stability.