Acoustic Emission Intelligent Monitoring of Diamond Grinding Wheel Wear Based on Empirical Mode Decomposition

GUO Li, HUO Deke, GUO Juntao


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.


Keywords: partially stabilized zirconia grinding,  diamond grinding wheel wear state monitoring,  acoustic emission,  empirical mode decomposition,  least squares support vector machine

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NIE P, DONG H, LI Z Q, et al. State recognition of tool wear based on improved empirical mode decomposition and least squares sup– port vector machine [J]. Journal of Beijing University of Tecknology,2013,39(12):1784–1790. ( In Chinese)

NADAI M E, AGUIAR P R, GUILLARDI H, et al. Evaluation of neural models applied to the estimation of tool wear in the grinding of advanced ceramics [J]. Expert Systems with Applications, 2015, 42:7026–7035.

NIE P, XU H Y, LIU X Y,et al. Application of EEMD method in state recognition of tool wear [J]. Transducer and Microsystems Technologies, 2012, 31(5):147–149 (In Chinese)

LI Q,SONG B Q. Tool wear state diagnosis with IMF singular value entropy[J]. Manufacturing Automation, 2013, 35 (12):52 –55 (In Chinese)

YANG Z S. Research on the indirect monitoring technique of grinding burn and its interrelated problems in precision grinding process[D]. Hangzhou: Zhejiang University, 2013 (In Chinese)

SUN H B, NIU W L,BANG J Y. Tool wear feature extraction based on Hilbert –Huang transformation [J]. Journal of Vibration and Shock, 2015, 34 (4):158–164 (In Chinese)

GUAN S, PENG C. Chaotic characteristic analysis of tool wear a soustis emission signal [J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2015, 31 (11): 60–65. (In Chinese)

CHEN X,ÖPÖZ T T. Effest of different parameters on grinding efficiency and its monitoring by acoustics emission [J]. Production & Manufacturing Researsk, 2016, 4 (1):190–208.

PENG Y F, LIU Z T, CHENG J S, et al. Adaptive and sparsest time–frequency analysis method based on initial value optimization [J]. Journal of Hunan University(Natural Sciences), 2017, 44(8): 50–56 (In Chinese)

LIAO T B. Barren Liao. Feature extraction and selection from acoustic emission signals with an application in grinding wheel condition monitoring [J]. Engineering Applications of Artificial Intelligense, 2010,23:74–84.

SHI J,DING N. On –line detention of the state of grinding wheel wear based on acoustics emission technique [J]. Journal of Changsha University, 2013, 23 (8):931–936 (In Chinese)

MOHAMMED A, FOLDES J, CHEN X. Detention of grinding temperatures using laser irradiation and acoustics emission sensing technique [J]. Materials and Manufacturing Processes, 2012, 27 (4):395–400.


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