The Feature Extraction of Impact Response and Load Reconstruction Based on Impulse Response Theory

Abstract

Impact load is a kind of aperiodic excitation with a short action time and large amplitude, it had more significant effect on the structure than static load. The reconstruction (or identification namely) of impact load is of great importance for validating the structural strength. The aim of this article was to reconstruct the impact load accurately. An impact load identification method based on impulse response theory (IRT) and BP (Back Propagation) neural network is proposed. The excitation and response signals were transformed to the same length by extracting the peak value (amplitude of sine wave) in the rising oscillation period of the response. First, we deduced that there was an approximate linear relationship between the discrete-time integral of impact load and the amplitude of the oscillation period of the response. Secondly, a BP neural network was used to es-tablish a linear relationship between the discrete-time integral of the impact load and the peak value in the rising oscillation period of the response. Thirdly, the network was trained and verified. The error between the actual maximum amplitude of impact load and the identification value was 2.22%. The error between the actual equivalent impulse and the identification value was 0.67%. The results showed that this method had high accuracy and application potential.