Prediction of Fatigue Crack Growth Under Broadband Random Vibration Loading Based on Extended Kalman Filter Algorithm

ABSTRACT This paper investigates the crack propagation behavior of aluminum alloys under broadband random loading and proposes a life prediction framework based on the extended Kalman filter. The prior fatigue life model employs a frequency‐domain method to calculate the stress probability density function of broadband random signals. The extended Kalman filter framework is established by integrating measurement data from the early stage of crack propagation with the prior model, aiming to reduce the uncertainties. Vibration fatigue experiments were performed to evaluate the effectiveness of the proposed approach. Comparing the model with experimental results demonstrates that the extended Kalman filter algorithm enhances the accuracy of life prediction. Through the integration of real‐time health monitoring techniques, the proposed method is capable of accurately predicting crack growth life and facilitating the development of an effective maintenance strategy.