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. Footstep-induced signals can provide valuable information about 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 'many-to-many' LSTM model with a KLD regularizer and L1 regularization, which is effective in denoising structural vibration signals, particularly for regimes with larger amplitudes. The model was trained and tested using synthetic data generated by a single degree of freedom oscillator. Our results demonstrate that the proposed approach is effective in reducing noise in the signals, particularly for regimes with larger amplitudes. The approach is promising for a wide range of applications of footstep-induced structural vibration signals, including healthcare, security, and technology.

翻译：振动信号已越来越多地应用于各种工程领域，用于分析和监测目的，包括结构健康监测、故障诊断和损伤检测，其中振动信号可提供有关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增长趋势。活动引起的结构振动，特别是脚步引起的信号，有助于分析人体和动物等生物系统的运动。脚步引起的信号可提供关于个体步态、体重和姿势的宝贵信息，使其成为健康监测、安全和人机交互的有吸引力的工具。然而，各种噪声的存在会损害脚步信号分析的准确性。在本文中，我们提出了一种新颖的"多对多"LSTM模型，该模型结合了KLD正则化器和L1正则化，能有效去噪结构振动信号，尤其适用于振幅较大的情况。该模型使用由单自由度振荡器生成的合成数据进行训练和测试。我们的结果表明，所提出的方法能有效降低信号中的噪声，特别是对于振幅较大的情况。该方法在脚步引起的结构振动信号的广泛应用中具有前景，包括医疗保健、安全和技术领域。