Health monitoring of an assembly-type structure using raw time-domain EMI signals and a novel hybrid deep learning algorithm

Abstract

Deep learning models are widely used to extract features from data through supervised or unsupervised methods. However, when these two methods are used separately, their generalization ability is insufficient. In this study, a SHM technology based on a novel hybrid deep learning (HDL) model and time-domain electromechanical impedance (TEMI) has been proposed. The HDL is a highly efficient combination of deep auto-encoder (DAE) and two-dimensional convolutional neural network (2-D CNN). The DAE is used for unsupervised reconstruction of the high-level features of 1-D TEMI signals, and then, the features extracted by DAE are arranged and fed into a supervised 2-D CNN that is designed to learn the optimal feature representations of the multi-label signals. This increases effectively the model's learnability and generalizability. The robustness of the developed approach has been tested using TEMI signals that are collected from a scaled model of a rectangular pipe gallery subjected to transversal joint damage. Results indicate that the maximal identification errors of joint damage severity are 2.4% and 9.33% when the corresponding structural conditions are used and unused by the training process of HDL, respectively.