摘要
针对高速公路机电设备维护过程中人工效率低、管理不够精确的问题,设计一种基于FPGA的高速公路机电设备智能监测系统。首先,该系统以FPGA为主控芯片,结合STM32模块对设备状态信息进行多种传感器数据采集,其中传感器输出的信号利用放大滤波电路进行了调理。其次,利用分层卷积深度学习对采集到获得机电设备状态数据进行分析。通过残差网络替换传统的特征提取网络并建立候选区域的损失函数模型,实现了机电设备故障智能监测。实验结果表明,相比其他监测方法,该系统在噪声干扰下的故障漏警率得到大幅降低,仅为0.16%,满足了自动化管理工作的需求。
Aiming at the problems of low labor efficiency and inaccurate management in the maintenance process of expressway electromechanical equipment,an intelligent monitoring system of expressway electromechanical equipment based on FPGA is designed.At first,the system uses FPGA as the main control chip,and combines STM32 module to collect a variety of sensor data for equipment status information,in which the signal output by the sensor is conditioned by the amplification filter circuit.Secondly,hierarchical convolution deep learning is used to analyze the collected state data of electromechanical equipment.By replacing traditional feature extraction network with residual network and establishing loss function model of candidate region,intelligent fault detection of electromechanical equipment is realized.Experimental results show that,compared with other monitoring methods,the system′s failure alarm rate under noise interference is greatly reduced to only 0.16%,which meets the requirements of automatic management.
作者
郑梽浩
Zheng Zhihao(Guangdong Luo Yang Expressway Co.,Ltd.,Yangchun 529600,China)
出处
《电子测量技术》
北大核心
2021年第2期27-31,共5页
Electronic Measurement Technology
基金
广东省普通高校重点科研平台和项目(2019GCZX006)资助。
关键词
公路机电
设备智能监测
FPGA
STM32
卷积神经网络
road electromechanical
intelligent monitoring of equipment
FPGA
STM32
convolution neural network
