Structural Vibration Signal Denoising Using Stacking Ensemble of Hybrid
CNN-RNN
- URL: http://arxiv.org/abs/2303.11413v4
- Date: Sun, 23 Jul 2023 03:07:01 GMT
- Title: Structural Vibration Signal Denoising Using Stacking Ensemble of Hybrid
CNN-RNN
- Authors: Youzhi Liang, Wen Liang, Jianguo Jia
- Abstract summary: In recent years, there has been a growing trend towards the use of vibration signals in the field of bioengineering.
Footstep-induced vibrations are useful for analyzing the movement of biological systems such as the human body and animals.
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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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