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Fast-Activated Minimal Gated Unit: Lightweight Processing and Feature Recognition for Multiple Mechanical Impact Signals

Authors :
Wenrui Wang
Dong Han
Xinyi Duan
Yaxin Yong
Zhengqing Wu
Xiang Ma
He Zhang
Keren Dai
Source :
Sensors, Vol 24, Iss 16, p 5245 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Multiple dynamic impact signals are widely used in a variety of engineering scenarios and are difficult to identify accurately and quickly due to the signal adhesion phenomenon caused by nonlinear interference. To address this problem, an intelligent algorithm combining wavelet transforms with lightweight neural networks is proposed. First, the features of multiple impact signals are analyzed by establishing a transfer model for multiple impacts in multibody dynamical systems, and interference is suppressed using wavelet transformation. Second, a lightweight neural network, i.e., fast-activated minimal gated unit (FMGU), is elaborated for multiple impact signals, which can reduce computational complexity and improve real-time performance. Third, the experimental results show that the proposed method maintains excellent feature recognition results compared to gate recurrent unit (GRU) and long short-term memory (LSTM) networks under all test datasets with varying impact speeds, while its metrics for computational complexity are 50% lower than those of the GRU and LSTM. Therefore, the proposed method is of great practical value for weak hardware application platforms that require the accurate identification of multiple dynamic impact signals in real time.

Details

Language :
English
ISSN :
14248220
Volume :
24
Issue :
16
Database :
Directory of Open Access Journals
Journal :
Sensors
Publication Type :
Academic Journal
Accession number :
edsdoj.4f50eb161dda4f92a9b0f6233d907c58
Document Type :
article
Full Text :
https://doi.org/10.3390/s24165245