基于EMD的金刚石砂轮磨损状态声发射监测
|
摘要
针对磨削金刚石砂轮磨损状态声发射信号小波分析中存在的问题,根据工程陶瓷部分稳定氧化锆磨削过程中声发射信号非线性非平稳性的特点,采用经验模态分解方法将磨削声发射信号分解为多个平稳的固有模态函数之和,并提取其有效值、方差和能量系数等特征值.在磨削金刚石砂轮从轻度磨损状态转变为严重磨损状态时,固有模态函数的有效值(IMFrms)和方差(IMFvar)增大,而能量系数(IMFpe)发生明显的变化;将其做为最小二乘支持向量机的输入参数,对金刚石砂轮的轻度磨损状态和严重磨损状态成功地进行了智能监测.
In view of the existing problem in the wavelet analysis of acoustic emission signals in wear state of diamond grinding wheel,because engineering ceramics partially stabilized zirconia grinding acoustic emission signals have nonlinear and nonstationary characteristics,using empirical mode decomposition method the acoustic emission signals were decomposed into several stationary intrinsic mode functions and then the root mean squares,variances and energy coefficients were extracted. When the wear state of diamond grinding wheel changes from mild wear to severe wear,the root mean squares(IMFrms)and variances(IMFvar)of the intrinsic mode function increase,and the energy coefficients(IMFpe)change significantly. As the input parameter of the least squares support vector machine,the wear state of diamond grinding wheel was successfully monitored.
In view of the existing problem in the wavelet analysis of acoustic emission signals in wear state of diamond grinding wheel,because engineering ceramics partially stabilized zirconia grinding acoustic emission signals have nonlinear and nonstationary characteristics,using empirical mode decomposition method the acoustic emission signals were decomposed into several stationary intrinsic mode functions and then the root mean squares,variances and energy coefficients were extracted. When the wear state of diamond grinding wheel changes from mild wear to severe wear,the root mean squares(IMFrms)and variances(IMFvar)of the intrinsic mode function increase,and the energy coefficients(IMFpe)change significantly. As the input parameter of the least squares support vector machine,the wear state of diamond grinding wheel was successfully monitored.
引文
[1]聂鹏,董慧,李正强,等.基于改进EMD和LS-SVM的刀具磨损状态识别[J].北京工业大学学报,2013,39(12):1784-1790.NIE P,DONG H,LI Z Q,et al. State recognition of tool wear basedon improved empirical mode decomposition and least squares sup-port vector machine[J]. Journal of Beijing University of Technolo-gy,2013,39(12):1784-1790.(In Chinese)
[2] NAKAI M E,AGUIAR P R,GUILLARDI H,et al. Evaluation ofneural models applied to the estimation of tool wear in the grindingof advanced ceramics[J]. Expert Systems with Applications,2015,42:7026-7035.
[3]聂鹏,徐洪圭,刘新宇,等. EEMD方法在刀具磨损状态识别的应用[J].传感器与微系统,2012,31(5):147-149.NIE P,XU H Y,LIU X Y,et al. Application of EEMD method instate recognition of tool wear[J]. Transducer and MicrosystemTechnologies,2012,31(5):147-149.(In Chinese)
[4]李庆,宋万清. IMF奇异值熵的刀具磨损状态诊断[J].制造业自动化,2013,35(12):52-55.LI Q,SONG W Q. Tool wear state diagnosis with IMF singular val-ue entropy[J]. Manufacturing Automation,2013,35(12):52-55.(In Chinese)
[5]杨振生.面向磨削烧伤问题的间接监测技术研究[D].杭州:浙江大学,2013.YANG Z S. Research on the indirect monitoring technique ofgrinding burn and its interrelated problems in precision grindingprocess[D]. Hangzhou:Zhejiang University,2013.(In Chinese)
[6]孙惠斌,牛伟龙,王俊阳.基于希尔伯特黄变换的刀具磨损特征提取[J].振动与冲击,2015,34(4):158-164.SUN H B,NIU W L,WANG J Y. Tool wear feature extraction basedon Hilbert-Huang transformation[J]. Journal of Vibration andShock,2015,34(4):158-164.(In Chinese)
[7]关山,彭昶.刀具磨损声发射信号的混沌特性分析[J].农业工程学报,2015,31(11):60-65GUAN S,PENG C. Chaotic characteristic analysis of tool wear a-coustic emission signal[J]. Transactions of the Chinese Society ofAgricultural Engineering(Transactions of the CSAE),2015,31(11):60-65.(In Chinese)
[8] CHEN X,OPOZ T T. Effect of different parameters on grinding ef-ficiency and its monitoring by acoustic emission[J]. Production&Manufacturing Research,2016,4(1):190-208.
[9]彭延峰,刘贞涛,程军圣,等.基于初值优化的自适应最稀疏时频分析方法[J].湖南大学学报(自然科学版),2017,44(8):50-56.PENG Y F,LIU Z T,CHENG J S,et al. Adaptive and sparsesttime-frequency analysis method based on initial value optimization[J]. Journal of Hunan University(Natural Sciences),2017,44(8):50-56.(In Chinese)
[10] LIAO T W. Warren Liao. Feature extraction and selection from a-coustic emission signals with an application in grinding wheel con-dition monitoring[J]. Engineering Applications of Artificial Intelli-gence,2010,23:74-84.
[11]石建,丁宁.基于声发射技术的砂轮磨损状况在线检测[J].长春大学学报,2013,23(8):931-936.SHI J,DING N. On-line detection of the state of grinding wheelwear based on acoustic emission technique[J]. Journal ofChangchun University,2013,23(8):931-936.(In Chinese)
[12]MOHAMMED A,FOLKES J,CHEN X. Detection of grinding tem-peratures using laser irradiation and acoustic emission sensingtechnique[J]. Materials and Manufacturing Processes,2012,27(4):395-400.
[13]CHEN X,GRIFFIN J,LIU Q. Mechanical and thermal behaviors ofgrinding acoustic emission[J]. International Journal of Manufac-turing Technology and Management,2007,12(1/2/3):184-199.
[2] NAKAI M E,AGUIAR P R,GUILLARDI H,et al. Evaluation ofneural models applied to the estimation of tool wear in the grindingof advanced ceramics[J]. Expert Systems with Applications,2015,42:7026-7035.
[3]聂鹏,徐洪圭,刘新宇,等. EEMD方法在刀具磨损状态识别的应用[J].传感器与微系统,2012,31(5):147-149.NIE P,XU H Y,LIU X Y,et al. Application of EEMD method instate recognition of tool wear[J]. Transducer and MicrosystemTechnologies,2012,31(5):147-149.(In Chinese)
[4]李庆,宋万清. IMF奇异值熵的刀具磨损状态诊断[J].制造业自动化,2013,35(12):52-55.LI Q,SONG W Q. Tool wear state diagnosis with IMF singular val-ue entropy[J]. Manufacturing Automation,2013,35(12):52-55.(In Chinese)
[5]杨振生.面向磨削烧伤问题的间接监测技术研究[D].杭州:浙江大学,2013.YANG Z S. Research on the indirect monitoring technique ofgrinding burn and its interrelated problems in precision grindingprocess[D]. Hangzhou:Zhejiang University,2013.(In Chinese)
[6]孙惠斌,牛伟龙,王俊阳.基于希尔伯特黄变换的刀具磨损特征提取[J].振动与冲击,2015,34(4):158-164.SUN H B,NIU W L,WANG J Y. Tool wear feature extraction basedon Hilbert-Huang transformation[J]. Journal of Vibration andShock,2015,34(4):158-164.(In Chinese)
[7]关山,彭昶.刀具磨损声发射信号的混沌特性分析[J].农业工程学报,2015,31(11):60-65GUAN S,PENG C. Chaotic characteristic analysis of tool wear a-coustic emission signal[J]. Transactions of the Chinese Society ofAgricultural Engineering(Transactions of the CSAE),2015,31(11):60-65.(In Chinese)
[8] CHEN X,OPOZ T T. Effect of different parameters on grinding ef-ficiency and its monitoring by acoustic emission[J]. Production&Manufacturing Research,2016,4(1):190-208.
[9]彭延峰,刘贞涛,程军圣,等.基于初值优化的自适应最稀疏时频分析方法[J].湖南大学学报(自然科学版),2017,44(8):50-56.PENG Y F,LIU Z T,CHENG J S,et al. Adaptive and sparsesttime-frequency analysis method based on initial value optimization[J]. Journal of Hunan University(Natural Sciences),2017,44(8):50-56.(In Chinese)
[10] LIAO T W. Warren Liao. Feature extraction and selection from a-coustic emission signals with an application in grinding wheel con-dition monitoring[J]. Engineering Applications of Artificial Intelli-gence,2010,23:74-84.
[11]石建,丁宁.基于声发射技术的砂轮磨损状况在线检测[J].长春大学学报,2013,23(8):931-936.SHI J,DING N. On-line detection of the state of grinding wheelwear based on acoustic emission technique[J]. Journal ofChangchun University,2013,23(8):931-936.(In Chinese)
[12]MOHAMMED A,FOLKES J,CHEN X. Detection of grinding tem-peratures using laser irradiation and acoustic emission sensingtechnique[J]. Materials and Manufacturing Processes,2012,27(4):395-400.
[13]CHEN X,GRIFFIN J,LIU Q. Mechanical and thermal behaviors ofgrinding acoustic emission[J]. International Journal of Manufac-turing Technology and Management,2007,12(1/2/3):184-199.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |