An Integrated Method Based on Convolutional Neural Networks and Data Fusion for Assembled Structure State Recognition

Abstract

This article focuses on the Assembled Structure (AS) state recognition method based on vibration data. The difficulty of AS state recognition is mainly the extraction of effective classification features and pattern classification. This paper presents an integrated method based on Convolutional Neural Networks (CNNs) and data fusion for AS state recognition. The method takes the wavelet transform time-frequency images of the denoised vibration signal as input, uses CNNs to supervise and learn the data, extracts the deep data structure layer by layer, and improves the classification results through data fusion technology. The method is tested on an assembly concrete shear wall using shake-table testing, and the results show that it has a good overall identification accuracy (IA) of 94.7%, indicating that it is robust and capable of accurately recognizing very small changes in AS state recognition.