Impact load identification method based on frequency response pattern recognition and dynamic sensor filter strategy

Abstract

Identification of impact loads plays important role in marine structures health monitoring but is difficult to be measured directly most time. This study investigates a two-stage framework for impact load localization and reconstruction, consisting of load region identification and local refined nodal search. For the region identification, a novel frequency response feature preprocessing method based on FFT is proposed and incorporated into a multi-layer perceptron (MLP) neural network as the embedding function of the Matching Network (MN), the core model adopted for pattern recognition. Based on the region probabilities predicted by MN, a local refined nodal search strategy is provided, which is initialized by a region correction method for amending the possible region misclassification and further guided by error metrics with iteration search strategy. Moreover, the inverse problem in this study is formulated in the discretized state space expression with the reduced modal coordinates. For improving the load inverse accuracy affected by Zero Order Hold (ZOH) simplification in this formulation, a dynamic sensor filter strategy is provided. Eventually, a numerical experiment of impact load identification on a steel plate is performed and discussed, whose results indicate the validity and robustness of the proposed method.