面向环境状态监测的振动传感器系统信号辨识

针对振动传感器系统对环境状态中入侵事件识别正确率较低的难题，该文提出了一种基于随机配置网络(SCN)的神经网络结构,用于识别周界入侵的振动传感信号。首先通过搭建振动传感器系统对4种周界入侵信号进行采集,然后利用小波降噪对信号进行降噪预处理,再提取信号的能量特征、过均值率、PAR特征,最后通过随机配置网络神经网络对攀爬、触碰、撞击及剪切4种入侵事件进行识别。其训练集识别准确率可达92.7%，测试集平均识别准确率可达90.7%。实验结果表明，该文所提出方法可对周界入侵信号进行有效识别。

Abstract

To address the problem of low accuracy in identifying intrusion events in environmental states in the vibration systems, this paper proposes a SCN-based neural network structure for recognizing perimeter intrusion vibration sensor signals. Firstly, four types of perimeter intrusion signals are collected by building a vibration sensor system. Then, the wavelet noise reduction is used to pre-process the signals. After that, the energy features, over-average rate and PAR features of the signals are extracted. Finally, four intrusion events of climbing, touching, impacting and shearing events are recognized by a stochastic configuration network (SCN) neural network. The recognition accuracy of the training set reached 92.7%, and the average recognition accuracy of the test set reached 90.7%. The experimental results show that the proposed method can effectively identify the perimeter intrusion signals.

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王占生, 申晓明, 曾轶哲, 曾祥豹, 谢婷玉. 面向环境状态监测的振动传感器系统信号辨识[J]. 压电与声光, 2023, 45(6): 935. WANG Zhansheng, SHEN Xiaoming, ZENG Yizhe, ZENG Xiangbao, XIE Tingyu. Signal Identification Method of Vibration Sensor System for Environmental Condition Monitoring[J]. Piezoelectrics & Acoustooptics, 2023, 45(6): 935.

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