数字化制造装备的主轴服役性能监测与诊断
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摘要
数字化制造是先进制造的核心技术,实现制造过程的数字化定量描述是推进数字化制造的关键。主轴部件是数控装备的最重要部件之一,针对主轴中最易损伤的轴承,研究了新的时频分析方法S变换,获得主轴振动信号的时频分布,采用奇异值分解技术对所获得的时频矩阵进行数据压缩,从而定量提取振动信号的故障特征。数值仿真和试验研究结果表明该方法对于主轴轴承运行状态具有较高的识别精度,为数字化制造装备的主轴服役性能评估提供了新思路。
Digitized manufacturing is the kernel of advanced manufacturing technologies, and the realization of digitized and quantitative description of manufacturing process is the key to promoting the digitized manufacturing. Spindle assembly is one of the most important components of CNC equipment. Aiming at the bearings which are most vulnerable in the spindle system, a new time-frequency analysis method called S transform is studied, time-frequency distribution of vibration signals collected from spindle is obtained, and a singular value decomposition method is employed to condense the time-frequency matrix data so that the fault features can be extracted quantitatively. Simulation and experimental studies demonstrate that the proposed method can identify the running state of spindle bearing accurately. Thus a new idea for the evaluation of spindle service performance of digitized manufacturing equipments is provided.
Digitized manufacturing is the kernel of advanced manufacturing technologies, and the realization of digitized and quantitative description of manufacturing process is the key to promoting the digitized manufacturing. Spindle assembly is one of the most important components of CNC equipment. Aiming at the bearings which are most vulnerable in the spindle system, a new time-frequency analysis method called S transform is studied, time-frequency distribution of vibration signals collected from spindle is obtained, and a singular value decomposition method is employed to condense the time-frequency matrix data so that the fault features can be extracted quantitatively. Simulation and experimental studies demonstrate that the proposed method can identify the running state of spindle bearing accurately. Thus a new idea for the evaluation of spindle service performance of digitized manufacturing equipments is provided.
引文
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[12]占勇,程浩忠,丁屹峰.基于S变换的电能质量扰动支持向量机分类识别[J].中国电机工程学报,2005,25(4):51-56.ZHAN Yong,CHENG Haozhong,DING Yifeng,et al.S-transform-based classfication of power quality disturbance signals by support vector machines[J].Proceedings of the Csee,2005,25(4):51-56.
[13]高静怀,陈文超,李幼铭,等.广义S变换与薄互层地震响应分析[J].地球物理学报,2003,46(4):526-532.GAO Jinghuai,CHEN Wenchao,LI Youming,et al.Generalized S transform and seismic response analysis of thin interbeds[J].Chinese Journal of Geophysics,2003,46(4):526-532.
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[15]LOPARO K A.Bearings vibration data set,Case Western Reserve University[EB/OL].[2005-02-01].http://www.eecs.cwru.edu/laboratory/bearing/download.htm.
[16]屈梁生,何正嘉.机械故障诊断学[M].上海:上海科学技术出版社,1986.QU Liangsheng,HE Zhengjia.Introduction to machine fault diagnosis[M].Shanghai:Shanghai Scientific&Technical Publishers,1986.
[2]NAKKIEW W,LIN C W,TU J F.A new method to quantify radial error of a motorized end-milling cutter/spindle system at very high speed rotations[J].International Journal of Machine Tools&Manufacture,2006,46:877-889.
[3]PENG Zhike,CHU Fulei.Application of wavelet transform in machine condition monitoring and fault diagnostics:A review with bibliography[J].Mechanical Systems and Signal Processing,2004,18:199-221.
[4]CAO Y Z,ALTINTAS Y.Modeling of spindle-bearing and machine tool systems for virtual simulation of milling operations[J].International Journal of Machine Tools&Maufacture,2007,47:1342-1350.
[5]GAGNOLA V,BOUZGARROUA B C,RAYA P.Model-based chatter stability prediction for high-speed spindles[J].International Journal of Machine Tools&Maufacture,2007,47:1176-1186.
[6]STOCKWELL R G,MANSINHAL L R,LOWE P.Localization of the complex spectrum:The S transfor[J].IEEE Transactions on Signal Processing,1996,17:998-1001.
[7]STOCKWELL R G.A basis for efficient representation of the S-transform[J].Digital Signal Processing,2007,17:371-393.
[8]REHORN A G,SEJDIC E,JIANG J.Fault diagnosis in machine tools using selective regional correlation[J].Mechanical Systems and Signal Processing,2006,20(5):1221-1238.
[9]王成栋,张优云,夏勇.基于S变换的柴油机气阀机构故障诊断研究[J].内燃机学报,2003,21(4):271-275.WANG Chengdong,ZHANG Youyun,XIA Yong.Fault diagnosis for diesel valve train based on S transform[J].Transactions of Csice,2003,21(4):271-275.
[10]纪少波,程勇,杨滨,等.基于S变换的柴油机燃烧差异的诊断研究[J].车用发动机,2007(4):23-26.JI Shaobo,CHENG Yong,YANG Bin,et al.Study on diagnosis of diesel engine combustion difference based on S-transform[J].Vehicle Engine,2007(4):23-26.
[11]CHILUKURI M V,DASH P K.Multiresolution S-transform-based fuzzy recognition system for power quality events[J].IEEE Transactions on Power Delivery,2004,19(1):323-330.
[12]占勇,程浩忠,丁屹峰.基于S变换的电能质量扰动支持向量机分类识别[J].中国电机工程学报,2005,25(4):51-56.ZHAN Yong,CHENG Haozhong,DING Yifeng,et al.S-transform-based classfication of power quality disturbance signals by support vector machines[J].Proceedings of the Csee,2005,25(4):51-56.
[13]高静怀,陈文超,李幼铭,等.广义S变换与薄互层地震响应分析[J].地球物理学报,2003,46(4):526-532.GAO Jinghuai,CHEN Wenchao,LI Youming,et al.Generalized S transform and seismic response analysis of thin interbeds[J].Chinese Journal of Geophysics,2003,46(4):526-532.
[14]SCHIMMEL M,GALLART J.The inverse S-transform in filters with time-frequency localization[J].IEEE Transactions on Signal Processing,2005,53(11):4417-4422.
[15]LOPARO K A.Bearings vibration data set,Case Western Reserve University[EB/OL].[2005-02-01].http://www.eecs.cwru.edu/laboratory/bearing/download.htm.
[16]屈梁生,何正嘉.机械故障诊断学[M].上海:上海科学技术出版社,1986.QU Liangsheng,HE Zhengjia.Introduction to machine fault diagnosis[M].Shanghai:Shanghai Scientific&Technical Publishers,1986.
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