Method for Vibration Response Simulation and Sensor Placement Optimization of a Machine Tool Spindle System with a Bearing Defect

Bearing defects are one of the most important mechanical sources for vibration and noise generation in machine tool spindles. In this study, an integrated finite element (FE) model is proposed to predict the vibration responses of a spindle bearing system with localized bearing defects and then the sensor placement for better detection of bearing faults is optimized. A nonlinear bearing model is developed based on Jones' bearing theory, while the drawbar, shaft and housing are modeled as Timoshenko's beam. The bearing model is then integrated into the FE model of drawbar/shaft/housing by assembling equations of motion. The Newmark time integration method is used to solve the vibration responses numerically. The FE model of the spindle-bearing system was verified by conducting dynamic tests. Then, the localized bearing defects were modeled and vibration responses generated by the outer ring defect were simulated as an illustration. The optimization scheme of the sensor placement was carried out on the test spindle. The results proved that, the optimal sensor placement depends on the vibration modes under different boundary conditions and the transfer path between the excitation and the response.

[1]  Jin Chen,et al.  Weak fault feature extraction of rolling bearing based on cyclic Wiener filter and envelope spectrum , 2011 .

[2]  Yusuf Altintas,et al.  Virtual Design and Optimization of Machine Tool Spindles , 2005 .

[3]  P. D. McFadden,et al.  Model for the vibration produced by a single point defect in a rolling element bearing , 1984 .

[4]  Yanyang Zi,et al.  Multiwavelet denoising with improved neighboring coefficients for application on rolling bearing fault diagnosis , 2011 .

[5]  Huaqing Wang,et al.  A Feature Extraction Method Based on Information Theory for Fault Diagnosis of Reciprocating Machinery , 2009, Sensors.

[6]  Yang Yu,et al.  The application of energy operator demodulation approach based on EMD in machinery fault diagnosis , 2007 .

[7]  Aitzol Lamikiz,et al.  Machine Tools for High Performance Machining , 2009 .

[8]  Yaguo Lei,et al.  EEMD method and WNN for fault diagnosis of locomotive roller bearings , 2011, Expert Syst. Appl..

[9]  Aki Mikkola,et al.  Dynamic model of a deep-groove ball bearing including localized and distributed defects. Part 1: Theory , 2003 .

[10]  Yusuf Altintas,et al.  A comparative study on the dynamics of high speed spindles with respect to different preload mechanisms , 2011 .

[11]  Robert B. Randall,et al.  Vibration response of spalled rolling element bearings: Observations, simulations and signal processing techniques to track the spall size , 2011 .

[12]  Ligang Cai,et al.  Roller Bearing Fault Diagnosis Based on Nonlinear Redundant Lifting Wavelet Packet Analysis , 2010, Sensors.

[13]  T. A. Harris,et al.  Rolling Bearing Analysis , 1967 .

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

[15]  Robert B. Randall,et al.  Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast kurtogram , 2009 .

[16]  Yusuf Altintas,et al.  A General Method for the Modeling of Spindle-Bearing Systems , 2004 .

[17]  Robert B. Randall,et al.  The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines , 2006 .

[18]  Robert B. Randall,et al.  Simulating gear and bearing interactions in the presence of faults. Part I. The combined gear bearing dynamic model and the simulation of localised bearing faults , 2008 .

[19]  R. Randall,et al.  OPTIMISATION OF BEARING DIAGNOSTIC TECHNIQUES USING SIMULATED AND ACTUAL BEARING FAULT SIGNALS , 2000 .

[20]  J. Antoni The spectral kurtosis: a useful tool for characterising non-stationary signals , 2006 .

[21]  Robert X. Gao,et al.  Wavelet domain principal feature analysis for spindle health diagnosis , 2011 .

[22]  Hamid Moeenfard,et al.  Nonlinear dynamic modeling of surface defects in rolling element bearing systems , 2009 .

[23]  Ruqiang Yan,et al.  Harmonic wavelet-based data filtering for enhanced machine defect identification , 2010 .

[24]  Jose Mathew,et al.  Bearing Signature Analysis as a Medium for Fault Detection: A Review , 2008 .

[25]  Yuh-Tay Sheen,et al.  An envelope detection method based on the first-vibration-mode of bearing vibration , 2008 .

[26]  Robert B. Randall,et al.  Simulating gear and bearing interactions in the presence of faults Part II. Simulation of the vibrations produced by extended bearing faults , 2008 .

[27]  Yusuf Altintas,et al.  Modeling of spindle-bearing and machine tool systems for virtual simulation of milling operations , 2007 .

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

[29]  Robert B. Randall,et al.  Rolling element bearing diagnostics—A tutorial , 2011 .

[30]  N. Tandon,et al.  A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings , 1999 .

[31]  Christian Brecher,et al.  Machine tool spindle units , 2010 .

[32]  Liang-Cheng Chang,et al.  The Development of a Monitoring System Using a Wireless and Powerless Sensing Node Deployed Inside a Spindle , 2012, Sensors.

[33]  Peter J. Kootsookos,et al.  MODELING OF LOW SHAFT SPEED BEARING FAULTS FOR CONDITION MONITORING , 1998 .

[34]  Yaguo Lei,et al.  Application of an improved kurtogram method for fault diagnosis of rolling element bearings , 2011 .

[35]  N. Tandon,et al.  Vibration Response of Rolling Element Bearings in a Rotor Bearing System to a Local Defect Under Radial Load , 2006 .

[36]  David Brie,et al.  Modelling of the Spalled Rolling Element Bearing Vibration Signal: AN Overview and Some New Results , 2000 .

[37]  Robert B. Randall,et al.  A Stochastic Model for Simulation and Diagnostics of Rolling Element Bearings With Localized Faults , 2003 .

[38]  A. B. Jones A General Theory for Elastically Constrained Ball and Radial Roller Bearings Under Arbitrary Load and Speed Conditions , 1960 .

[39]  Aki Mikkola,et al.  Dynamic model of a deep-groove ball bearing including localized and distributed defects. Part 2: Implementation and results , 2003 .

[40]  Sébastien Seguy,et al.  Chatter milling modeling of active magnetic bearing spindle in high-speed domain , 2011 .

[41]  Ruqiang Yan,et al.  Permutation entropy: A nonlinear statistical measure for status characterization of rotary machines , 2012 .