基于电流的主轴性能退化评估方法
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摘要
为提高精密机床主轴检测的准确性,提出了一种基于电流的主轴性能退化评估方法.建立了主轴性能退化模型,使主轴状态便于监测和评估.首先采用小波包阈值对电流信号进行去噪处理,对去噪后的电流信号提取时频域特征量,构造多域特征空间.然后利用主成分分析法(PCA)进行数据降维,用降维后的样本进行支持向量机回归建模.采用粒子群算法(PSO)对支持向量机模型进行参数优化,以获得最优性能退化模型.将该模型应用于主轴实验台主轴性能退化评估,实验结果表明该方法原理正确,可以准确评价主轴性能.
In order to improve the accuracy of spindle detection for precision machine tools, a method was proposed based on current for evaluating the performance degradation of spindle. A performance degradation model of the spindle was established to facilitate the monitoring and evaluation of the spindle condition. Firstly, the wavelet packet threshold was used to denoise the current signal, and then the multi domain feature space was constructed by extracting the time-frequency features of the denoised current signal. Then the principal component analysis(PCA) was used for data dimensionality reduction, and the dimensionality reduction of samples was used for support vector machine regression modeling. the particle swarm optimization(PSO) algorithm was used to optimize the parameters of the support vector machines(SVM) model to obtain the optimal performance degradation model. Finally, the model was applied to the evaluation of spindle performance degradation in a experiment platform. The experimental results show that the method is correct and can accurately evaluate spindle performance.
In order to improve the accuracy of spindle detection for precision machine tools, a method was proposed based on current for evaluating the performance degradation of spindle. A performance degradation model of the spindle was established to facilitate the monitoring and evaluation of the spindle condition. Firstly, the wavelet packet threshold was used to denoise the current signal, and then the multi domain feature space was constructed by extracting the time-frequency features of the denoised current signal. Then the principal component analysis(PCA) was used for data dimensionality reduction, and the dimensionality reduction of samples was used for support vector machine regression modeling. the particle swarm optimization(PSO) algorithm was used to optimize the parameters of the support vector machines(SVM) model to obtain the optimal performance degradation model. Finally, the model was applied to the evaluation of spindle performance degradation in a experiment platform. The experimental results show that the method is correct and can accurately evaluate spindle performance.
引文
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[2] 吴远昊.基于电流分析法的车床刀具状态监测技术研究 [D].西安:西南交通大学,2017.Wu Yuanhao.Research on condition monitoring of lathe tools on motor current signal analysis [D] .Xi’an:Southwest Jiaotong University,2017.(in Chinese)
[3] Niklas P,Ebeerhard A,Stefano S.A power disaggregation approach for finegrained machine energy monitoring by sy- stem identification [J].Procedia CIRP,2016,48:325-330.
[4] 王华忠,杨智慧,颜秉勇,等. 融合PCA和PSO-SVM方法在工控入侵检测中的应用[J].科技通报,2017,33(1):80-85.Wang Huazhong,Yang Zhihui,Yan Bingyong,et al.Application of fusion PCA and PSO-SVM method in industrial control intru-sion detection[J].Bulletin of science and technology,2017,33(1):80-85.(in Chinese)
[5] 陈昌,汤宝平,吕中亮.基于威布尔分布及最小二乘支持向量机的滚动轴承退化趋势预测[J].振动与冲击,2014,33(20):52-56. Chen Chang,Tang Baoping,Lü Zhongliang.Degradation trend prediction of rolling bearings based on Weibull distribution and least squares support vector machine [J].Journal of Vibration and Shock,2014,33(20):52-56.(in Chinese)
[6] 黄海凤,高宏力,许明恒,等.数控机床主轴系统性能退化评估方法 [J].振动、测试与诊断,2013,33(4):646-652.Huang Haifeng,Gao Hongli,Xu Mingheng,et al.Evaluation method of performance degradation for NC machine tool spindle system [J].Journal of Vibration,Measurement & Diagnosis,2013,33(4):646-652.(in Chinese)
[7] 黄海凤,高宏力,许明恒,等.基于振动信号的数控机床丝杠副性能退化研究 [J].机械科学与技术,2013,32(5):631-635.Huang Haifeng,Gao Hongli,Xu Mingheng,et al.The performance degradation based on the vibration of NC machine tool screw pair [J].Mechanical Science and Technology for Aerospace Engineering,2013,32(5):631-635.(in Chinese)
[8] 陈园艺,孙建平.基于PSO-SVR和SPC的风电机组齿轮箱故障预警研究 [J].电力科学与工程,2016,32(5):43-48.Chen Yuanyi,Sun Jianping.Research on fault early warning of wind turbine gear box based on PSO-SVR and SPC [J].Electric Power Science and Engineering,2016,32(5):43-48.(in Chinese)
[9] 万鹏,王红军,徐小力.局部切空间排列和支持向量机的故障诊断模型 [J].仪器仪表学报,2012,33(12):2789-2795.Wan Peng,Wang Hongjun,Xu Xiaoli.Fault diagnosis model based on local tangent space alignment and support vector machine[J].Chinese Journal of Scientific Instrument,2012,33(12):2789-2795.(in Chinese)
[10] 彭润玲,刘鹏,曾群锋,等.TiNi60合金与钢球和陶瓷球配副的摩擦学性能对比分析[J].润滑与密封,2016,41(4):95-100.Peng Runling,Liu Peng,Zeng Qunfeng,et al.Comparative analysis of tribological properties of TiNi60 alloy with steel balls and ceramic balls [J].Lubrication Engineering,2016,41(4):95-100.(in Chinese)
[11] 王红军,万鹏.基于EEMD和小波包变换的早期故障敏感特征获取[J].北京理工大学学报,2013,33(9):945-950.Wang Hongjun,Wan Peng.Sensitive features extraction of early fault based on EEMD and WPT [J]Transactions of Beijing Institute of Technology,2013,33(9):945-950.(in Chinese)
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