吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (2): 392-399.doi: 10.13229/j.cnki.jdxbgxb20211154

• 车辆工程·机械工程 • 上一篇    

基于PSO-SVM及时序环节的数控刀架故障诊断方法

罗巍1,2(),卢博3,陈菲4(),马腾1,2   

  1. 1.吉林大学 数控装备可靠性教育部重点实验室，长春 130022
    2.吉林大学 机械与航空航天工程学院，长春 130022
    3.长春设备工艺研究所，长春 130012
    4.深圳技术大学 中德智能制造学院，深圳 518118
  • 收稿日期:2021-11-04 出版日期:2022-02-01 发布日期:2022-02-17
  • 通讯作者: 陈菲 E-mail:luoweicn@jlu.edu.cn;chenfei@sztu.edu.cn
  • 作者简介:罗巍（1986-），女，副研究员，博士.研究方向：数控装备可靠性及故障诊断.E-mail：luoweicn@jlu.edu.cn
  • 基金资助:
    吉林省教育厅项目(JJKH20211068KJ)

Fault diagnosis method of NC turret based on PSO⁃SVM and time sequence

Wei LUO1,2(),Bo LU3,Fei CHEN4(),Teng MA1,2   

  1. 1.Key Laboratory of CNC Equipment Reliability，Ministry of Education，Jilin University，Changchun 130022，China
    2.College of Mechanical and Aerospace Engineering，Jilin University，Changchun 130022，China
    3.Changchun Equipment & Technology Research Institute，Changchun 130012，China
    4.Sino-German College of Intelligent Manufacturing，Shenzhen Technology University，Shenzhen 518118，China
  • Received:2021-11-04 Online:2022-02-01 Published:2022-02-17
  • Contact: Fei CHEN E-mail:luoweicn@jlu.edu.cn;chenfei@sztu.edu.cn

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摘要：

提出了一种基于粒子群-支持向量机（PSO-SVM）及时序环节的数控刀架故障诊断方法。首先，将数控刀架划分为5个子系统，并将一个工作周期划分为4个时序环节（T1、T2、T3、T4）；其次，探索了数控刀架不同时序环节振动、电机电流、油压以及接近开关等信号的特征提取方法；最后，提出了基于PSO-SVM的数控刀架故障诊断方法，并开展了不同时序环节的数控刀架故障试验。根据故障数据对支持向量机（SVM）和PSO-SVM两种故障诊断方法进行了对比验证。结果表明：时序环节T2、T3和T4的故障诊断准确率分别提高了28%、23%和5%，验证了该故障诊断方法的有效性。本文方法不仅适用于数控刀架，还为其他复杂机电系统的故障诊断研究提供了一个新思路。

关键词: 数控刀架, 支持向量机, 粒子群算法, 时序环节, 故障诊断

Abstract:

A fault diagnosis method of NC turret based on particle swarm optimization and support vector machine （PSO-SVM） is proposed. Firstly， the NC turret is divided into five subsystems， and a working cycle is divided into four time sequences T1， T2， T3 and T4. Secondly， the feature extraction methods of vibration， motor current， oil pressure and proximity switch signal in different time sequences of NC turret are explored. Finally， fault diagnosis method of NC turret based on PSO-SVM was proposed， and NC turret fault tests were carried out in different time sequences. According to the fault data， support vector machine （SVM） and PSO-SVM fault diagnosis methods are compared and verified. The results show that the fault diagnosis accuracy of T2， T3 and T4 are increased by 28%， 23% and 5%， respectively， which verifies the validity of the proposed fault diagnosis method. The fault diagnosis method proposed in this paper is not only suitable for NC turret， but also provides a new idea for the fault diagnosis of other complex electromechanical system.

Key words: NC turret, support vector machine, particle swarm optimization, time sequence, fault diagnosis

中图分类号: 

  • TH133.33

图1

数控刀架子系统划分示意图"

图2

数控刀架工作周期信号"

图3

数控刀架一个工作周期信号示意图"

表1

数控刀架不同时序环节的关键子系统和敏感信号表"

时序环节关键子系统敏感信号
T1液压子系统、发讯子系统油压信号、振动、接近开关信号
T2驱动子系统、传动子系统电流、振动
T3发讯子系统、液压子系统、辅助结构子系统油压信号、振动、接近开关信号
T4辅助结构子系统振动

图4

PSO-SVM算法的步骤"

图5

数控刀架PSO-SVM故障诊断方法流程示意图"

图6

数控刀架可靠性试验系统1-信号采集系统显示器；2-24 V电源；3-NI采集卡；4-数显式压力变送器；5-上位机控制界面；6-刀架安装台；7-数控刀架；8-伺服加载机构；9-伺服加载控制器"

图7

时序环节T1和T2故障试验"

图8

时序环节T3故障试验"

图9

时序环节T4刀具夹持松动试验"

图10

基于PSO-SVM的时序环节T1 故障诊断结果"

图11

基于PSO-SVM的时序环节T2 故障诊断结果"

图12

基于PSO-SVM的时序环节T3 故障诊断结果"

图13

基于PSO-SVM的时序环节T4 故障诊断结果"

表2

SVM和PSO-SVM的两种模型诊断结果"

时序环节测试样本数SVMPSO-SVM准确率
Cγ诊断准确率/%Cγ诊断准确率/%
T12410.07100.0043.09940.01100.00
T23210.0778.1378.01006.74100.00
T33610.0777.7811.83910.0195.84
T44010.0795.0070.08000.01100.00
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