The invention discloses a
bogie component
vibration fatigue life prediction method considering load periodic evolution, and aims to solve the technical problems that an existing model is coupled in function and low in prediction precision, and a prediction result is a single determined value and cannot meet
risk assessment requirements. Comprising the following steps: a) acquiring a historical power
spectral density sequence; b) inputting the historical power
spectral density sequence into a
hybrid degradation prediction model, and predicting axle box vibration power
spectral density under any running mileage in the future; c) inputting the axle box vibration power spectral density predicted in the step b) into a
nonlinear transfer function model based on
artificial intelligence, and calculating the stress power spectral density of the key position of the
bogie; d) dynamically determining an optimal calculation mileage step length by the decision-making type
artificial intelligence model, and then calculating future prediction
fatigue damage; and e) calculating generated actual
fatigue damage according to historical
monitoring data, and outputting probability distribution of the residual fatigue life of the
bogie by integrating future predicted
fatigue damage.