电子测量与仪器学报

魏许杰,王红军,邢济收,徐小力.基于 CGA-SVR 的电主轴磨损故障诊断方法研究[J].电子测量与仪器学报,2022,36(6):107-112

基于 CGA-SVR 的电主轴磨损故障诊断方法研究

Research on wear fault diagnosis of motorized spindle based on CGA-SVR

DOI：

英文关键词:motorized spindle fault diagnosis support vector machine for regression chaos genetic algorithm

基金项目:国家自然科学基金(51575055)、北京市科技计划项目(Z201100008320004)、科技重大专项项目( 2015ZX04001002)资助

作者	单位
魏许杰	1. 北京信息科技大学机电工程学院，2. 高端装备智能感知与控制北京市国际科技合作基地
王红军	1. 北京信息科技大学机电工程学院，2. 高端装备智能感知与控制北京市国际科技合作基地
邢济收	1. 北京信息科技大学机电工程学院，2. 高端装备智能感知与控制北京市国际科技合作基地
徐小力	1. 北京信息科技大学机电工程学院，2. 高端装备智能感知与控制北京市国际科技合作基地

Author	Institution
Wei Xujie	1. School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University,2. High-end Equipment Intelligent Perception and Control Beijing International Science and Technology Cooperation Base(BISTU)
Wang Hongjun	1. School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University,2. High-end Equipment Intelligent Perception and Control Beijing International Science and Technology Cooperation Base(BISTU)
Xing Jishou	1. School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University,2. High-end Equipment Intelligent Perception and Control Beijing International Science and Technology Cooperation Base(BISTU)
Xu Xiaoli	1. School of Mechanical and Electrical Engineering, Beijing Information Science and Technology University,2. High-end Equipment Intelligent Perception and Control Beijing International Science and Technology Cooperation Base(BISTU)

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中文摘要:

电主轴是数控机床的一个重要功能部件,其优劣直接影响着工件质量,对电主轴进行故障诊断可以提高可靠性、降低生产成本。因此采用混沌遗传算法(CGA) 优化的支持向量机回归模型( SVR) 进行电主轴故障诊断。此方法利用主成分分析 (PCA)对电主轴磨损故障振动信号的时、频域特征向量进行降维,将降维后的特征向量输入到经过 CGA 参数优化的 SVR 模型中并进行训练和测试。结果表明,使用该模型对电主轴进行故障诊断,其训练和测试的准确率分别达到了 99. 272% 和 95. 249%,可以实现对电主轴磨损故障进行准确诊断。

英文摘要:

Motorized spindle is an important functional part of CNC machine tool, and its advantages and disadvantages directly affect the quality of parts. A support vector machine regression model ( SVR) optimized by chaos genetic algorithm (CGA) is used for spindle fault diagnosis. The principle of the method is to use principal component analysis ( PCA) to reduce the dimensionality of the timefrequency characteristic vector of the vibration signal of electric spindle wear fault, and input the dimensionality reduced characteristic vector into the SVR model optimized by CGA parameters for training and testing. The results show that the accuracy of training and testing is 99. 272% and 95. 249% respectively, which can diagnose the wear fault of motorized spindle accurately.

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