基于ARM的嵌入式轴承健康监测系统设计

针对传统轴承健康监测系统集成度低、体积较大的问题,设计了一套基于ARM的嵌入式轴承健康监测系统。系统基于ARM9内核的S3C2440A主控芯片、Linux操作系统与Qt图形化开发软件,实现了轴承状态信号采集、实时分析处理、LCD显示、上传云服务器、状态报警与故障诊断功能。通过小波包能量熵提取故障特征,可以准确地反映出故障聚集的频段,并将其输入到RBF神经网络实现故障类型的自动识别,提高了诊断的质量和效率。试验结果表明,系统数据可靠、通信效果好、故障诊断准确率高,满足实际应用的需求。

Design of Embedded Bearing Health Monitoring System Based on ARM

Aiming at the problems of low integration and large volume of traditional bearing health monitoring system,an embedded bearing health monitoring system based on ARM was designed.The system realized the functions of bearing status signal acquisition,real-time analysis and processing,LCD display,upload to cloud server,status alarm and fault diagnosis based on the ARM9 core S3C2440A main control chip,Linux operating system and Qt graphical development software.Extracting fault features through wavelet packet energy entropy can accurately reflect the frequency bands where faults were gathered and input them to the RBF neural network to automatically identify fault types,which improved the quality and efficiency of diagnosis.The experiment results show that the system data is reliable,the communication effect is good,and the accuracy of fault diagnosis is high,which meets the needs of practical applications.