Innovative Signal Processing: Stacking CNN-RNN for Structural Vibration Denoising

Abstract

This research introduces an innovative signal processing approach for structural vibration denoising, leveraging the power of stacked Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The proposed method aims to enhance the extraction of relevant features from vibration signals, allowing for effective noise reduction. The stacking of CNNs and RNNs creates a hybrid model capable of capturing both spatial and temporal dependencies in the data. Through extensive experimentation and validation, this study demonstrates the effectiveness of the proposed approach in denoising structural vibration signals, showcasing its potential applications in fields such as structural health monitoring and condition-based maintenance.