Deep reference autoencoder convolutional neural network for damage identification in parallel steel wire cables

This paper addresses the challenge of overcoming the limited availability of training samples for the damage state of parallel steel wire cables in practical engineering and the difficulty of detecting minor damages. To tackle this issue, we propose an unsupervised deep learning damage identification technique called Deep Reference Autoencoder Convolutional Neural Network (DRACNN) for analyzing the damage state of parallel steel wire cables in bridge engineering. The DRACNN method utilizes multi-dimensional cross-correlation function (CCF) derived from acceleration signals at various health stages as input to train the network structure and obtain optimal parameters. Subsequently, we analyze the layer decomposition to identify neurons in the lowest hidden layer indicating damage. The neuronal change information is then extracted using an Exponentially Weighted Moving Average (EWMA) Control Chart to determine the damage state of the structure. Finally, we present a comprehensive numerical analysis describing the method's flow and network architecture and demonstrate the feasibility of this approach through experiments.