A hybrid support vector regression with multi-domain features for low-velocity impact localization on composite plate structure

Abstract

The accurate localization of low-velocity impacts on the composite plate structure of the ship is still a great challenge. Current research mainly focuses on extracting single domain features from impact signals as the input of machine learning methods, whereas ignores multi-domain features with more comprehensive impact information. In this paper, a hybrid support vector regression with multi-domain features is proposed to increase the localization accuracy in determining the locations of low-velocity impacts on the composite plate structure. The proposed method consists of the signal preprocessing, the multi-domain feature extraction, and the impact localization. In the signal preprocessing, the trend component in the low-velocity impact signals is eliminated by adopting the empirical mode decomposition (EMD) method. Then, the multi-domain features, which include time domain features, frequency domain features, and time-frequency domain features, are extracted from the preprocessed impact signals. Finally, the optimized support vector regression based on the bat algorithm (BA-SVR) is designed to implement the localization of low-velocity impacts. The low-velocity impact localization system using four fiber Bragg grating (FBG) sensors is established on a carbon fiber reinforced plastic (CFRP) plate, and then five sets of experiments are executed. The statistical results in these experiments demonstrate the effectiveness and feasibility of BA-SVR that uses multi-domain features and four FBG sensors and the satisfactory localization performance of the proposed method in handling the low-velocity impact localization problem on the CFRP plate.