Status Monitoring Technology for Machining Center Spindle System Based on Wavelet De-Noising and EMD–SVM Algorithms

LI Guofa; WANG Dachuan; ZHANG Xin’ge; DU Le; DONG Jinghua

doi:10.16080/j.issn1671–833x.2019.06.047

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Status Monitoring Technology for Machining Center Spindle System Based on Wavelet De-Noising and EMD–SVM Algorithms

更新时间：2025-10-30

- Status Monitoring Technology for Machining Center Spindle System Based on Wavelet De-Noising and EMD–SVM Algorithms
- Aeronautical Manufacturing Technology Vol. 62, Issue 6, Pages: 47-52(2019)
- 作者机构：
  
  1. 吉林大学机械工业数控装备可靠性技术重点实验室,长春,130025
  2. 北京北一机床股份有限公司,北京,101300
- 作者简介：
- 基金信息：
- DOI：10.16080/j.issn1671–833x.2019.06.047
  CLC：
- Published：2019
- 稿件说明：
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李国发，王大川，张新戈，杜乐，董精华. 基于小波降噪和EMD-SVM的加工中心主轴系统状态监测技术[J]. 航空制造技术, 2019, 62(6): 47-52.

LI Guofa, WANG Dachuan, ZHANG Xin’ge, DU Le, DONG Jinghua. Status Monitoring Technology for Machining Center Spindle System Based on Wavelet De-Noising and EMD–SVM Algorithms. Aeronautical Manufacturing Technology, 2019, 62(6): 47-52.
李国发，王大川，张新戈，杜乐，董精华. 基于小波降噪和EMD-SVM的加工中心主轴系统状态监测技术[J]. 航空制造技术, 2019, 62(6): 47-52. DOI： 10.16080/j.issn1671–833x.2019.06.047.

LI Guofa, WANG Dachuan, ZHANG Xin’ge, DU Le, DONG Jinghua. Status Monitoring Technology for Machining Center Spindle System Based on Wavelet De-Noising and EMD–SVM Algorithms. Aeronautical Manufacturing Technology, 2019, 62(6): 47-52. DOI： 10.16080/j.issn1671–833x.2019.06.047.

摘要

主轴系统是数控机床的重要功能部件，其运行状态直接影响机床的可靠性与加工精度。为了实现状态实时监测、故障预警和维修策略优化，针对加工中心主轴系统，设计了状态监测方案，研制和搭建了加工中心状态监测平台的硬件系统和软件系统。集成小波降噪方法和经验模态分解– 支持向量机（EMD–SVM）算法对采集信号处理与分析，实现加工中心主轴系统的状态实时监测，进而对主轴系统典型故障状态进行识别与诊断。基于研制的加工中心主轴状态监测系统，进行了主轴系统皮带松动故障监测试验分析，验证其对主轴系统的典型故障状态识别的准确度。

Abstract

The Spindle system is an important functional component of CNC machine tool

and its operating status directly affect the reliability of machine tool and the machining accuracy of parts. In order to achieve real-time monitoring

fault warning and maintenance strategy optimization

a status monitoring scheme was designed for machining center spindle system

and the hardware and software systems of status monitoring platform were developed and built. The wavelet de-noising method and empirical mode decomposition (EMD)-support vector machine (SVM) algorithms were used to process and analyze the signals

so as to achieve the status real-time monitoring and diagnosis of typical fault status for the machining center spindle system. Based on the spindle status monitoring system

the spindle belt looseness fault status monitoring test was carried out

and the accuracy of recognizing the typical fault status of the spindle system was verified.

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