摘要
研究基于单片机的充填机机电一体化系统故障诊断方法,提升充填机机电一体化系统的故障诊断精度。选取TMS320C54X单片机作为充填机机电一体化系统的核心处理器,TMS320C54X单片机利用内置的智能处理模型预处理传感器采集的充填机机电一体化系统运行信号,滤波处理所采集的信号;选取EMD分解方法处理完成滤波处理后信号,提取充填机机电一体化系统的故障信号特征向量;选取GRNN神经网络依据所提取的故障特征向量实现故障诊断,利用CAN总线通信方式传送故障诊断结果至充填机机电一体化系统的远程控制中心,实现充填机机电一体化系统故障诊断。实验结果表明,该方法可以有效诊断充填机机电一体化系统不同类型故障,故障诊断精度高于98%,故障平均诊断时间均低于220 ms。
The fault diagnosis method of filling machine mechatronics system based on single chip microcomputer is studied to improve the fault diagnosis accuracy of filling machine mechatronics system.The TMS320C54X single-chip microcomputer is selected as the core processor of the filling machine mechatronics system.The TMS320C54X single-chip microcomputer uses the built-in intelligent processing model to preprocess the operation signal of the filling machine mechatronics system collected by the sensor, filter and process the collected signal, and select the EMD decomposition method to complete the processing.After filtering the processed signal, the fault signal feature vector of the filling machine mechatronics system is extracted, the GRNN neural network is selected to realize the fault diagnosis based on the extracted fault feature vector, and the CAN bus communication method is used to transmit the fault diagnosis result to the filling machine mechatronics system’s remote control center to realize fault diagnosis of filling machine mechatronics system.The experimental results show that the method can effectively diagnose different types of faults in the mechanical and electrical integration system of the filling machine, the fault diagnosis accuracy is higher than 98%,and the average fault diagnosis time is less than 220 ms.
作者
王艳华
WANG Yanhua(Yantai Vocational College,Yantai 264670,China)
出处
《机械与电子》
2023年第2期76-80,共5页
Machinery & Electronics
关键词
单片机
充填机
机电一体化
故障诊断
EMD分解
GRNN神经网络
single chip microcomputer
filling machine
mechatronics
fault diagnosis
EMD decomposition
GRNN neural network
