基于线性可分SVM的自动化机床在线故障监测系统设计

针对自动化机床在线故障监测存在的问题，设计一种基于线性可分SVM的故障监测系统。在线监测系统的硬件结构由STM32F103ZET6型单片机、传感器模块、存储器模块、通信模块和显示模块等部分组成；在软件算法流程上，利用线性可分SVM分类器，可以确保数据集到最优超平面的几何间隔最大，同时提升距离最优超平面最近数据点的可信度及对故障样本的分类精度。测试结果显示:设计的系统数据训练收敛速度快，故障数据分类精度高，相对于传统监测系统具有性能上的优势。

Design of On-line Fault Monitoring System for Automatic Machine Tool Based on Linear Separable SVM

Aiming at the problem of on-line fault monitoring of automatic machine tools,a monitoring system based on linear separable SVM was designed.The hardware structure of the monitoring system included STM32F103ZET6 single chip microcomputer,sensor module,memory module,communication module and display module.In the software algorithm flow,the linear separable SVM classifier could ensure the maximum geometric interval from the data set to the optimal hyperplane,improve the reliability of the data points closest to the optimal hyperplane and the classification accuracy of fault samples.The test results show that the proposed system has fast data training convergence speed,high fault data classification accuracy and performance advantages over the traditional monitoring system.