An approach based on wavelet packet decomposition and Hilbert-Huang transform (WPD-HHT) for spindle bearings condition monitoring

Abstract In order to prevent possible damages to the spindle systems, reliable monitoring techniques are required to provide valuable information on the condition of the spindle condition. A technique is proposed for monitoring spindle bearings conditions via the use of acoustic emission (AE) signals, which implements Hilbert–Huang transform (HHT) analysis to extract the crucial characteristic from the measured data to correlate spindle running condition. The HHT becomes a promising technique in extracting the properties of nonlinear and non-stationary signal. However, the original HHT has several deficiencies, which eventually lead to misinterpretation to the final results. The improved version of HHT is proposed and used to overcome the weakness of the original HHT. The simulation and experimental results are used to verify the effectiveness of the WPD–HHT and therefore Hilbert marginal spectral, compared to traditional Fourier transform. Experimental results are presented to examine and explore the effectiveness of AE for monitoring spindle bearings conditions. It is concluded that good correlation existed between the results obtained by AE data and the increase in the preload, and change in the dimensions and geometry of the spindle bearings and their housings as the temperature increases. In support of this finding, vibration and acceleration data are also used to assess the amount changes in the antistrophic stiffness and radial error motion.

[1]  Geok Soon Hong,et al.  Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results , 2009 .

[2]  Heber Ferreira Franco de Castro,et al.  A method for evaluating spindle rotation errors of machine tools using a laser interferometer , 2008 .

[3]  Robert X. Gao,et al.  Performance enhancement of ensemble empirical mode decomposition , 2010 .

[4]  N. Huang,et al.  A new view of nonlinear water waves: the Hilbert spectrum , 1999 .

[5]  Yoshimi Ito,et al.  Thermal Behavior of Machine Tool Spindle System Based on the Thermal Closed-Loop Concept. (Remedies for Thermal Deformation by Using the High-Speed Spindle Adapter). , 1991 .

[6]  Changyou Li,et al.  An Algorithm for Improving Hilbert-Huang Transform , 2007, International Conference on Computational Science.

[7]  Yoshimi Ito,et al.  Termal behavior of main spindle driving system with electromagnetic disk clutch of dry single plate type in machine tool. , 1990 .

[8]  Cc Tan.  Application of Acoustic Emission to the Detection of Bearing Failures , 1990 .

[9]  C. James Li,et al.  Acoustic emission analysis for bearing condition monitoring , 1995 .

[10]  Choon-Man Lee,et al.  A review on the preload technology of the rolling bearing for the spindle of machine tools , 2010 .

[11]  Robert X. Gao,et al.  Spindle Health Diagnosis Based on Analytic Wavelet Enveloping , 2006, IEEE Transactions on Instrumentation and Measurement.

[12]  Yoshimi Ito,et al.  Thermal Behavior of Bearing Surroundings in Machine Tool Spindle System. , 1991 .

[13]  P. Tse,et al.  An improved Hilbert–Huang transform and its application in vibration signal analysis , 2005 .

[14]  P D McFadden,et al.  Acoustic Emission Transducers for the Vibration Monitoring of Bearings at Low Speeds , 1984 .

[15]  Yaguo Lei,et al.  Application of the EEMD method to rotor fault diagnosis of rotating machinery , 2009 .

[16]  David,et al.  A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size , 2006 .

[17]  Robert X. Gao,et al.  Hilbert–Huang Transform-Based Vibration Signal Analysis for Machine Health Monitoring , 2006, IEEE Transactions on Instrumentation and Measurement.

[18]  R. Ogden,et al.  Essential Wavelets for Statistical Applications and Data Analysis , 1996 .

[19]  F P Wardle,et al.  Vibration Forces Produced by Waviness of the Rolling Surfaces of Thrust Loaded Ball Bearings Part 2: Experimental Validation , 1988 .

[20]  F P Wardle,et al.  Vibration Forces Produced by Waviness of the Rolling Surfaces of Thrust Loaded Ball Bearings Part 1: Theory , 1988 .

[21]  Yung C. Shin,et al.  Dynamics of Machine Tool Spindle/Bearing Systems Under Thermal Growth , 1996, Dynamic Systems and Control.

[22]  P. Tse,et al.  Machine fault diagnosis through an effective exact wavelet analysis , 2004 .

[23]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[24]  Peter W. Tse,et al.  Faulty bearing signal recovery from large noise using a hybrid method based on spectral kurtosis and ensemble empirical mode decomposition , 2012 .

[25]  L. D. Meyer,et al.  An Analytic Model for Ball Bearing Vibrations to Predict Vibration Response to Distributed Defects , 1980 .

[26]  N. Tandon,et al.  A comparison of some vibration parameters for the condition monitoring of rolling element bearings , 1994 .

[27]  B. C. Nakra,et al.  Defect detection in rolling element bearings by acoustic emission method , 1990 .

[28]  N. Tandon,et al.  Application of acoustic emission technique for the detection of defects in rolling element bearings , 2000 .

[29]  H. E. Staph,et al.  Thermally Activated Seizure of Angular Contact Bearings , 1967 .

[30]  H J Pahk,et al.  Thermal error measurement and modelling techniques for the five-degree-of-freedom spindle drifts in computer numerically controlled machine tools , 2001 .

[31]  Ioannis Antoniadis,et al.  Rolling element bearing fault diagnosis using wavelet packets , 2002 .

[32]  Michael I. Friswell,et al.  Defect Diagnosis for Rolling Element Bearings Using Acoustic , 2009 .

[33]  P. Tse,et al.  A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing , 2005 .

[34]  Jun Wang,et al.  Real-time tool condition monitoring using wavelet transforms and fuzzy techniques , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[35]  K.-J. Lee,et al.  Effect of Bearing Surroundings on the High-Speed Spindle-Bearing Compliance , 2002 .

[36]  Mahbubur Rahman,et al.  Modeling, measurement and error compensation of multi-axis machine tools. Part I: theory , 2000 .

[37]  Aun-Neow Poo,et al.  Error compensation in machine tools — a review: Part II: thermal errors , 2000 .

[38]  James B. Bryan,et al.  International Status of Thermal Error Research (1990) , 1990 .

[39]  D Mba,et al.  Condition monitoring of slow-speed rolling element bearings using stress waves , 2001 .

[40]  Homer Rahnejat,et al.  Bearing induced vibration in precision high speed routing spindles , 2000 .

[41]  Alan V. Oppenheim,et al.  Discrete-Time Signal Pro-cessing , 1989 .

[42]  M. Weck,et al.  Spindle Bearing Systems for High-Speed Applications in Machine Tools , 1993 .