摘要
该文主要探究云加端SVM模型在机电设备故障诊断中的应用。该文首先介绍云加端SVM模型的整体架构,并从原始数据获取、特征数据提取与降维处理及故障诊断算法等方面,概述该诊断技术的应用流程。随后将该文设计的基于并行流水线结构的云加端SVM模型与传统顺序结构云加端SVM模型进行准确度和实时性对比实验。结果表明,该文设计的云加端SVM模型经过在线学习后,故障诊断准确率提升到96.50%,单位时间内故障诊断效率是传统模型的4倍,可满足故障实时诊断的需要。
This paper mainly discusses the application of Cloud Plus Terminal SVM model in electromechanical equipment fault diagnosis.First of all,this paper introduces the overall architecture of the Cloud Plus Terminal SVM model,and summarizes the application flow of the diagnosis technology from the aspects of original data acquisition,feature data extraction and dimensionality reduction,and fault diagnosis algorithm.Then the accuracy and real-time performance of the Cloud Plus Terminal SVM model based on parallel pipeline structure and the traditional sequential structure cloud SVM model are compared.The results show that after online learning,the Cloud Plus Terminal SVM model designed in this paper can improve the fault diagnosis accuracy to 96.50%,and the fault diagnosis efficiency per unit time is 4 times that of the traditional model,which can meet the needs of real-time fault diagnosis.
出处
《科技创新与应用》
2023年第33期86-89,共4页
Technology Innovation and Application
关键词
云加端
SVM模型
机电设备
故障诊断
并行流水线结构
Cloud Plus Terminal
SVM model
electromechanical equipment
fault diagnosis
parallel pipeline structure
