基于改进的自适应渐消UKF机床主轴热平衡试验
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摘要
数控机床的主轴热平衡试验是进行热误差建模和补偿的必要手段,是准确获得数控机床主轴的热敏感点、温度场和热位移以及热平衡时间等热态特性的方法。本文提出一种基于改进的自适应渐消无迹卡尔曼滤波(Adaptive fading unscented Kalman filter,AFUKF)的快速辨识机床主轴选点温升的方法。首先,在标准UKF中引入渐消因子,使用残差归一化自动更新渐消因子,并将其引入增益矩阵,增强测量值在计算中的权重;其次,通过使用自适应规则,动态调整过程噪声和测量噪声协方差阵,减少外部扰动对温升预测的影响,以获得更好的滤波性能。仿真结果表明,提出的机床主轴温升快速辨识方法可以在很短的时间内预测选点的温升,且预测结果与热平衡试验结果吻合,验证了本文方法的可行性和有效性。
Thermal equilibrium test of CNC machine tool spindle is a necessary step in thermal error modeling and compensating,and also an experimental method to obtain the thermal characteristics of spindle system, such as the thermal sensitive points, the data of temperature field and thermaldisplacement field and so on. A novel method was presented for fast identification of a machine tool spindle temperature rise,based on a modified adaptive fading unscented Kalman filter( AFUKF).Firstly,a fading factor was introduced into the normal UKF. This factor can be automatically updated by using the residual normalization,and it was also introduced into the gain matrix to reduce the influence of system model deviation on estimation accuracy and enhance the stability of the filter. Secondly,by using adaptive law,the process noise and measurement noise covariance matrix were dynamically adjusted to reduce the influence of external disturbance on temperature rise prediction,so that the better filtering performance can be obtained. A vertical machine tool was used to validate the effectiveness of the presented method. Taking any selected point,we could identify the temperature rise at the point in28 min. The root mean square error( RMSE) between the estimated and measured temperatures in the period of 400 min was 0. 129 1℃,and the error between the estimated and measured steady-state temperature was 0. 097℃. The simulation experiments showed that the method of fast identification of machine tool spindle temperature rise can predict the temperature rise of the selected point in a short time,and the prediction results were in good agreement with the results of thermal equilibrium test. The feasibility and validity of the method were verified,and it can greatly improve the efficiency of thermal equilibrium test.
Thermal equilibrium test of CNC machine tool spindle is a necessary step in thermal error modeling and compensating,and also an experimental method to obtain the thermal characteristics of spindle system, such as the thermal sensitive points, the data of temperature field and thermaldisplacement field and so on. A novel method was presented for fast identification of a machine tool spindle temperature rise,based on a modified adaptive fading unscented Kalman filter( AFUKF).Firstly,a fading factor was introduced into the normal UKF. This factor can be automatically updated by using the residual normalization,and it was also introduced into the gain matrix to reduce the influence of system model deviation on estimation accuracy and enhance the stability of the filter. Secondly,by using adaptive law,the process noise and measurement noise covariance matrix were dynamically adjusted to reduce the influence of external disturbance on temperature rise prediction,so that the better filtering performance can be obtained. A vertical machine tool was used to validate the effectiveness of the presented method. Taking any selected point,we could identify the temperature rise at the point in28 min. The root mean square error( RMSE) between the estimated and measured temperatures in the period of 400 min was 0. 129 1℃,and the error between the estimated and measured steady-state temperature was 0. 097℃. The simulation experiments showed that the method of fast identification of machine tool spindle temperature rise can predict the temperature rise of the selected point in a short time,and the prediction results were in good agreement with the results of thermal equilibrium test. The feasibility and validity of the method were verified,and it can greatly improve the efficiency of thermal equilibrium test.
引文
[1]邓小雷,林欢,王建臣,等.机床主轴热设计研究综述[J].光学精密工程,2018,26(6):1415-1429.DENG Xiaolei,LIN Huan,WANG Jianchen,et al.Review on thermal design of machine tool spindles[J].Optics and Precision Engineering,2018,26(6):1415-1429.(in Chinese)
[2]MAYR J,JEDRZEJEWSKI J,UHLMANN E,et al.Thermal issues in machine tools[J].CIRP Annals-Manufacturing Technology,2012,61(2):771-791.
[3]ABELE E,ALTINTAS Y,BRECHER C.Machine tool spindle units[J].CIRP Annals,2010,59(2):781-802.
[4]ZIVKOVIC A,ZELJKOVIC M,TABAKOVIC S,et al.Mathematical modeling and experimental testing of high-speed spindle behavior[J].International Journal of Advanced Manufacturing Technology,2015,77(5-8):1071-1086.
[5]UHLMANN E,HU J.Thermal modeling of a high speed motor spindle[J].Procedia CIRP,2012(1):313-318.
[6]ZHANG J F,FENG P F,CHEN C,et al.A method for thermal performance modeling and simulation of machine tools[J].International Journal of Advanced Manufacturing Technology,2013,68(5-8):1517-1527.
[7]MA C,YANG J,ZHAO L,et al.Simulation and experimental study on the thermally induced deformations of high-speed spindle system[J].Applied Thermal Engineering,2015,86:251-268.
[8]MATSUO M,YASUI T,INAMURA T,et al.High-speed test of thermal effects for a machine-tool structure based on modalanalysis[J].Precision Engineering,1986,8(2):72-78.
[9]XIA C H,FU J Z,XU Y T,et al.Machine tool selected point temperature rise identification based on operational thermal modal analysis[J].International Journal of Advanced Manufacturing Technology,2014,70(1-4):19-31.
[10]冯刚,夏晨晖,孙磊,等.基于非线性预测的机床主轴温升特性快速辨识[J/OL].农业机械学报,2015,46(6):341-348.FENG Gang,XIA Chenhui,SUN Lei,et al.Fast identification of machine tool spindle temperature rise characteristics based on nonlinear prediction[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2015,46(6):341-348.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?file_no=20150649&flag=1&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2015.06.049.(in Chinese)
[11]JULIER S J,UHLMANN J K.Unscented filtering and nonlinear estimation[J].Proceeding of the IEEE,2004,92(3):401-422.
[12]MINASE J,LU T F,CAZZOLATO B,et al.Adaptive identification of hysteresis and creep in piezoelectric stack actuators[J].International Journal of Advanced Manufacturing Technology,2010,46(9-12):913-921.
[13]WANG W D,WANG X T,XIANG C L,et al.Unscented Kalman filter-based battery SOC estimation and peak power prediction method for power distribution of hybrid electric vehicles[J].IEEE Access,2018,6:35957-35965.
[14]XING Y J,HE W,PECHT M,et al.State of charge estimate of lithium-ion batteries using the open-circuit voltage at various ambient temperatures[J].Applied Energy,2014,113:106-115.
[15]REGULSKI P,TERZIJA V.Estimation of frequency and fundamental power components using an unscented Kalman filter[J].IEEE Transactions on Instrumentation and Measurement,2012,61(4):952-962.
[16]LI M H,HONG B R,LUO R H,et al.A novel method for mobile robot simultaneous localization and mapping[J].Journal of Zhejiang University(Science A),2006,7(6):937-944.
[17]JIANG Z,SONG Q,HE Y Q,et al.A novel adaptive unscented Kalman filter for nonlinear estimation[C]∥The 46th IEEEConference on Decision and Control,2007:5805-5810.
[18]阮承治,赵德安,刘晓洋,等.基于IPSO-UKF的水草清理作业船组合导航定位方法[J/OL].农业机械学报,2017,48(7):38-45.RUAN Chengzhi,ZHAO Dean,LIU Xiaoyang,et al.Integrated navigation positioning method based on IPSO-UKF for aquatic plants cleaning workboat[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2017,48(7):38-45.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?file_no=20170705&flag=1&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2017.07.005.(in Chinese)
[19]HU C,CHEN W,CHEN Y,et al.Adaptive Kalman filtering for vehicle navigation[J].Journal of Global Positioning Systems,2003,2(1):42-47.
[20]LOEBIS D,SUTTON R,CHUDLEY J,et al.Adaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUVnavigation system[J].Control Engineering Practice,2004,12(2):1531-1539.
[21]孙宇新,沈启康,叶海涵,等.基于改进UKF的无轴承异步电机无速度传感器控制[J].农业工程学报,2018,34(19):74-81.SUN Yuxin,SHEN Qikang,YE Haihan,et al.Speed-sensorless control system of bearingless induction motor based on modified adaptive fading unscented Kalman filter[J].Transactions of the CSAE,2018,34(19):74-81.(in Chinese)
[22]胡志坤,刘斌,林勇,等.电池SOC的自适应平方根无极卡尔曼滤波估计算法[J].电机与控制学报,2014,18(4):111-116.HU Zhikun,LIU Bin,LIN Yong,et al.Adaptive square root unscented Kalman filter for SOC estimation of battery[J].Electric Machines and Control,2014,18(4):111-116.(in Chinese)
[23]孙宇新,杨玉伟.无轴承异步电机非线性滤波器自适应逆解耦控制[J].农业工程学报,2016,32(14):76-83.SUN Yuxin,YANG Yuwei.Adaptive inverse decoupling control for bearingless induction motors based on nonlinear filter[J].Transactions of the CSAE,2016,32(14):76-83.(in Chinese)
[24]DAN S.Optimal state estimation:Kalman,h∞,and nonlinear approaches[M]∥SIMON D J.Optimal state estimation:Kalman,Infinity,and nonlinear approaches,2006.
[25]贺姗,赵旭,师昕.改进的强跟踪求积分卡尔曼滤波算法[J].计算机技术与发展,2018,28(7):43-47.HE Shan,ZHAO Xu,SHI Xin.Improved strong tracing quadrature Kalman filtering algorithm[J].Computer Technology and Development,2018,28(7):43-47.(in Chinese)
[26]ISO.ISO230-3 test code for machine tool part 3:determination of thermal effects[S].2007.
[2]MAYR J,JEDRZEJEWSKI J,UHLMANN E,et al.Thermal issues in machine tools[J].CIRP Annals-Manufacturing Technology,2012,61(2):771-791.
[3]ABELE E,ALTINTAS Y,BRECHER C.Machine tool spindle units[J].CIRP Annals,2010,59(2):781-802.
[4]ZIVKOVIC A,ZELJKOVIC M,TABAKOVIC S,et al.Mathematical modeling and experimental testing of high-speed spindle behavior[J].International Journal of Advanced Manufacturing Technology,2015,77(5-8):1071-1086.
[5]UHLMANN E,HU J.Thermal modeling of a high speed motor spindle[J].Procedia CIRP,2012(1):313-318.
[6]ZHANG J F,FENG P F,CHEN C,et al.A method for thermal performance modeling and simulation of machine tools[J].International Journal of Advanced Manufacturing Technology,2013,68(5-8):1517-1527.
[7]MA C,YANG J,ZHAO L,et al.Simulation and experimental study on the thermally induced deformations of high-speed spindle system[J].Applied Thermal Engineering,2015,86:251-268.
[8]MATSUO M,YASUI T,INAMURA T,et al.High-speed test of thermal effects for a machine-tool structure based on modalanalysis[J].Precision Engineering,1986,8(2):72-78.
[9]XIA C H,FU J Z,XU Y T,et al.Machine tool selected point temperature rise identification based on operational thermal modal analysis[J].International Journal of Advanced Manufacturing Technology,2014,70(1-4):19-31.
[10]冯刚,夏晨晖,孙磊,等.基于非线性预测的机床主轴温升特性快速辨识[J/OL].农业机械学报,2015,46(6):341-348.FENG Gang,XIA Chenhui,SUN Lei,et al.Fast identification of machine tool spindle temperature rise characteristics based on nonlinear prediction[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2015,46(6):341-348.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?file_no=20150649&flag=1&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2015.06.049.(in Chinese)
[11]JULIER S J,UHLMANN J K.Unscented filtering and nonlinear estimation[J].Proceeding of the IEEE,2004,92(3):401-422.
[12]MINASE J,LU T F,CAZZOLATO B,et al.Adaptive identification of hysteresis and creep in piezoelectric stack actuators[J].International Journal of Advanced Manufacturing Technology,2010,46(9-12):913-921.
[13]WANG W D,WANG X T,XIANG C L,et al.Unscented Kalman filter-based battery SOC estimation and peak power prediction method for power distribution of hybrid electric vehicles[J].IEEE Access,2018,6:35957-35965.
[14]XING Y J,HE W,PECHT M,et al.State of charge estimate of lithium-ion batteries using the open-circuit voltage at various ambient temperatures[J].Applied Energy,2014,113:106-115.
[15]REGULSKI P,TERZIJA V.Estimation of frequency and fundamental power components using an unscented Kalman filter[J].IEEE Transactions on Instrumentation and Measurement,2012,61(4):952-962.
[16]LI M H,HONG B R,LUO R H,et al.A novel method for mobile robot simultaneous localization and mapping[J].Journal of Zhejiang University(Science A),2006,7(6):937-944.
[17]JIANG Z,SONG Q,HE Y Q,et al.A novel adaptive unscented Kalman filter for nonlinear estimation[C]∥The 46th IEEEConference on Decision and Control,2007:5805-5810.
[18]阮承治,赵德安,刘晓洋,等.基于IPSO-UKF的水草清理作业船组合导航定位方法[J/OL].农业机械学报,2017,48(7):38-45.RUAN Chengzhi,ZHAO Dean,LIU Xiaoyang,et al.Integrated navigation positioning method based on IPSO-UKF for aquatic plants cleaning workboat[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2017,48(7):38-45.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?file_no=20170705&flag=1&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2017.07.005.(in Chinese)
[19]HU C,CHEN W,CHEN Y,et al.Adaptive Kalman filtering for vehicle navigation[J].Journal of Global Positioning Systems,2003,2(1):42-47.
[20]LOEBIS D,SUTTON R,CHUDLEY J,et al.Adaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUVnavigation system[J].Control Engineering Practice,2004,12(2):1531-1539.
[21]孙宇新,沈启康,叶海涵,等.基于改进UKF的无轴承异步电机无速度传感器控制[J].农业工程学报,2018,34(19):74-81.SUN Yuxin,SHEN Qikang,YE Haihan,et al.Speed-sensorless control system of bearingless induction motor based on modified adaptive fading unscented Kalman filter[J].Transactions of the CSAE,2018,34(19):74-81.(in Chinese)
[22]胡志坤,刘斌,林勇,等.电池SOC的自适应平方根无极卡尔曼滤波估计算法[J].电机与控制学报,2014,18(4):111-116.HU Zhikun,LIU Bin,LIN Yong,et al.Adaptive square root unscented Kalman filter for SOC estimation of battery[J].Electric Machines and Control,2014,18(4):111-116.(in Chinese)
[23]孙宇新,杨玉伟.无轴承异步电机非线性滤波器自适应逆解耦控制[J].农业工程学报,2016,32(14):76-83.SUN Yuxin,YANG Yuwei.Adaptive inverse decoupling control for bearingless induction motors based on nonlinear filter[J].Transactions of the CSAE,2016,32(14):76-83.(in Chinese)
[24]DAN S.Optimal state estimation:Kalman,h∞,and nonlinear approaches[M]∥SIMON D J.Optimal state estimation:Kalman,Infinity,and nonlinear approaches,2006.
[25]贺姗,赵旭,师昕.改进的强跟踪求积分卡尔曼滤波算法[J].计算机技术与发展,2018,28(7):43-47.HE Shan,ZHAO Xu,SHI Xin.Improved strong tracing quadrature Kalman filtering algorithm[J].Computer Technology and Development,2018,28(7):43-47.(in Chinese)
[26]ISO.ISO230-3 test code for machine tool part 3:determination of thermal effects[S].2007.
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