在单片机中应用卷积神经网络实现故障诊断
CNN Based Fault Diagnosis of Rolling Bearings Using MCU
张岷涛 1廖文豪 1卿朝进1
作者信息
- 1. 西华大学电气与电子信息学院,成都 610039
- 折叠
摘要
作者利用深度神经网络进行滚动轴承的智能故障诊断(IFD),将人工智能在低成本小型化平台上实现了应用.作者在文章中优化改进了二维神经网络(CNN2D)的神经网络架构,并将其部署到STM32H743VI单片机,实现了轴承故障振动信号的识别和分类.网络的训练和验证使用凯斯西储大学(CWRU)轴承故障数据集,并获得其中的包含 10 种故障类型的数据.使用基于Tensorflow深度学习框架的Keras工具对CNN2D的神经网络进行训练.验证可知该改进模型对故障识别准确度可以达到 98.90%.利用CubeAI工具将网络部署至单片机内.通过串口与电脑进行通信获取随机轴承数据,实测每次诊断运行时间为约为 19 ms.
Abstract
The deep neural network is applied to intelligent fault diagnosis(IFD)of rolling bearings,and artificial intelligence is realized in low cost miniaturized platform in this paper.The author optimized and improved a neural network architecture of two-dimensional convolutional neural network(CNN2D),and deployed it to STM32H743VI MCU to realize the identification and classification of bearing fault vibration signals.The training and validation of the network uses the Case Western Reserve University(CWRU)bearing fault data set and obtains data containing 10 fault types.The neural network of CNN2D is trained by Keras tool based on Tensorflow deep learning framework.Verification shows that the accuracy of fault identification can reach 98.90%.Then CubeAI tool is used to deploy the network to the microcontroller.The random bearing data is obtained through communication between serial port and computer,and the measured running time of each diagnosis is about 19 ms.
关键词
故障诊断/二维卷积神经网络/滚动轴承/KerasKey words
intelligent fault diagnosis(IFD)/CNN2D/rolling bearing/Keras引用本文复制引用
基金项目
四川省科技计划项目(2021JDRC0003)
出版年
2024
国家科技期刊平台
NSTL
万方数据