Vibration signals have been increasingly utilized in various engineering fields for analysis and monitoring purposes, including structural health monitoring, fault diagnosis and damage detection, where vibration signals can provide valuable information about the condition and integrity of structures. In recent years, there has been a growing trend towards the use of vibration signals in the field of bioengineering. Activity-induced structural vibrations, particularly footstep-induced signals, are useful for analyzing the movement of biological systems such as the human body and animals, providing valuable information regarding an individual's gait, body mass, and posture, making them an attractive tool for health monitoring, security, and human-computer interaction. However, the presence of various types of noise can compromise the accuracy of footstep-induced signal analysis. In this paper, we propose a novel ensemble model that leverages both the ensemble of multiple signals and of recurrent and convolutional neural network predictions. The proposed model consists of three stages: preprocessing, hybrid modeling, and ensemble. In the preprocessing stage, features are extracted using the Fast Fourier Transform and wavelet transform to capture the underlying physics-governed dynamics of the system and extract spatial and temporal features. In the hybrid modeling stage, a bi-directional LSTM is used to denoise the noisy signal concatenated with FFT results, and a CNN is used to obtain a condensed feature representation of the signal. In the ensemble stage, three layers of a fully-connected neural network are used to produce the final denoised signal. The proposed model addresses the challenges associated with structural vibration signals, which outperforms the prevailing algorithms for a wide range of noise levels, evaluated using PSNR, SNR, and WMAPE.

翻译：振动信号已越来越多地应用于各工程领域的分析与监测，包括结构健康监测、故障诊断与损伤检测，其中振动信号能够提供关于结构状态和完整性的宝贵信息。近年来，振动信号在生物工程领域的应用呈现增长趋势。活动引起的结构振动，特别是脚步诱发的信号，有助于分析人体和动物等生物系统的运动，为个体步态、体重和姿势提供有价值的信息，使其成为健康监测、安全和人机交互的有力工具。然而，各类噪声的存在会损害脚步诱发信号分析的准确性。本文提出了一种新颖的集成模型，该模型同时利用了多个信号的集成以及循环神经网络和卷积神经网络预测的集成。所提模型包含三个阶段：预处理、混合建模和集成。在预处理阶段，通过快速傅里叶变换和小波变换提取特征，以捕捉系统受物理规律支配的动力学特性，并提取空间和时间特征。在混合建模阶段，利用双向长短期记忆网络对与快速傅里叶变换结果拼接的含噪信号进行去噪，并使用卷积神经网络获取信号的紧凑特征表示。在集成阶段，采用三层全连接神经网络生成最终的去噪信号。该模型解决了结构振动信号面临的挑战，在广泛的噪声水平下优于主流算法，并通过峰值信噪比、信噪比和加权平均绝对百分比误差进行评估。