Method for vibration response simulation and sensor placement optimization of a machine tool spindle system with a bearing defect

Sensors (Switzerland)

Volumn 12, Issue 7, 2012, Pages 8732-8754

  Cao, Hongrui ^a   Niu, Linkai ^a   He, Zhengjia ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

Bearing defects; Finite element model; Sensor placement optimization; Spindle; Vibration simulation

Indexed keywords

EID: 84864353885     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s120708732     Document Type: Article

References (42)

1. López de Lacalle, L.N.; Lamiki, A. Machine Tools for High Performance Machining; Springer-Verlag London Limited: London, UK, 2009; pp. 84-105.
2. Abele, E.; Altintas, Y.; Brecher, C. Machine tool spindle units. CIRP Ann. Manuf. Technol. 2010, 59, 781-802.
3. Zhang, L.; Gao, R.X.; Lee, K.B. Spindle health diagnosis based on analytic wavelet enveloping. IEEE Trans. Instrum. Meas. 2006, 55, 1850-1858.
4. Gourc, E.; Seguy, S.; Arnaud, L. Chatter milling modeling of active magnetic bearing spindle in high-speed domain. Int. J. Mach. Tool Manuf. 2011, 51, 928-936.
5. Tandon, N.; Choudhury, A. A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribol. Int. 1999, 32, 469-480.
6. Patil, M.S.; Mathew, J.; RajendraKumar, P.K. Bearing signature analysis as a medium for fault detection: A review. ASME Trans. J. Tribol. 2008, 130, 14001.
7. Randall, R.B.; Antoni, J. Rolling element bearing diagnostics-A tutorial. Mech. Syst. Sign. Process. 2011, 25, 485-520.
8. Sawalhi, N.; Randall, R.B. Vibration response of spalled rolling element bearings: Observations, simulations and signal processing techniques to track the spall size. Mech. Syst. Sign. Process. 2011, 25, 846-870.
9. Sheen, Y.T. An envelope detection method based on the first-vibration-mode of bearing vibration. Measurement 2008, 41, 797-809.
10. Ming, Y.; Chen, J.; Dong, G. Weak fault feature extraction of rolling bearing based on cyclic Wiener filter and envelope spectrum. Mech. Syst. Sign. Process. 2011, 25, 1773-1785.
11. Yan, R.; Gao, R.X. Harmonic wavelet-based data filtering for enhanced machine defect identification. J. Sound Vib. 2010, 329, 3203-3217.
12. Gao, L.; Yang, Z.; Cai, L.; Wang, H.; Chen, P. Roller bearing fault diagnosis based on nonlinear redundant lifting wavelet packet analysis. Sensors 2011, 11, 260-277.
13. Wang, X.; Zi, Y.; He, Z. Multiwavelet denoising with improved neighboring coefficients for application on rolling bearing fault diagnosis. Mech. Syst. Sign. Process. 2011, 25, 285-304.
14. Yan, R.; Gao, R.X. Hilbert-Huang transform-based vibration signal analysis for machine health monitoring. IEEE Trans. Instrum. Meas. 2006, 55, 2320-2329.
15. Cheng, J.; Yu, D.; Yu, Y. The application of energy operator demodulation approach based on EMD in machinery fault diagnosis. Mech. Syst. Sign. Process. 2007, 21, 668-677.
16. Lei, Y.; He, Z.; Zi, Y. EEMD method and WNN for fault diagnosis of locomotive roller bearings. Expert Syst. Appl. 2011, 38, 7334-7341.
17. Antoni, J. The spectral kurtosis: A useful tool for characterising non-stationary signals. Mech. Syst. Sign. Process. 2006, 20, 282-307.
18. Antoni, J.; Randall, R.B. The spectral kurtosis: Application to the vibratory surveillance and diagnostics of rotating machines. Mech. Syst. Sign. Process. 2006, 20, 308-331.
19. Zhang, Y.; Randall, R.B. Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast kurtogram. Mech. Syst. Sign. Process. 2009, 23, 1509-1517.
20. Lei, Y.; Lin, J.; He, Z.; Zi, Y. Application of an improved kurtogram method for fault diagnosis of rolling element bearings. Mech. Syst. Sign. Process. 2011, 25, 1738-1749.
21. Wang, H.; Chen, P. A feature extraction method based on information theory for fault diagnosis of reciprocating machinery. Sensors 2009, 9, 2415-2436.
22. Yan, R.; Liu, Y.; Gao, R.X. Permutation entropy: A nonlinear statistical measure for status characterization of rotary machines. Mech. Syst. Sign. Process. 2012, 29, 474-484.
23. Mcfadden, P.D.; Smith, J.D. Model for the vibration produced by a single point-defect in a rolling element bearing. J. Sound Vib. 1984, 96, 69-82.
24. Wang, Y.F.; Kootsookos, P.J. Modeling of low shaft speed bearing faults for condition monitoring. Mech. Syst. Sign. Process. 1998, 12, 415-426.
25. Ho, D.; Randall, R.B. Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals. Mech. Syst. Signal Process. 2000, 14, 763-788.
26. Brie, D. Modelling of the spalled rolling element bearing vibration signal: An overview and some new results. Mech. Syst. Signal Process. 2000, 14, 353-369.
27. Antoni, J.; Randall, R.B. A stochastic model for simulation and diagnostics of rolling element bearings with localized faults. ASME Trans. J. Vib. Acoust. 2003, 125, 282-289.
28. Choudhury, A.; Tandon, N. Vibration response of rolling element bearings in a rotor bearing system to a local defect under radial load. ASME Trans. J. Tribol. 2006, 128, 252-261.
29. Sopanen, J.; Mikkola, A. Dynamic model of a deep-groove ball bearing including localized and distributed defects. Part 1: Theory. Proc. Institut. Mech. Eng. K J. Multi Body Dynam. 2003, 217, 201-211.
30. Sopanen, J.; Mikkola, A. Dynamic model of a deep-groove ball bearing including localized and distributed defects. Part 2: Implementation and results. Proc. Institut. Mech. Eng. Part K: J. Multi-Body Dynam. 2003, 217, 213-223.
31. Sawalhi, N.; Randall, R.B. 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. Mech. Syst. Sign. Process. 2008, 22, 1924-1951.
32. Sawalhi, N.; Randall, R.B. Simulating gear and bearing interactions in the presence of faults: Part II: Simulation of the vibrations produced by extended bearing faults. Mech. Syst. Sign. Process. 2008, 22, 1952-1966.
33. Rafsanjani, A.; Abbasion, S.; Farshidianfar, A.; Moeenfard, H. Nonlinear dynamic modeling of surface defects in rolling element bearing systems. J. Sound Vib. 2009, 319, 1150-1174.
34. Yan, R.; Gao, R.X. Wavelet domain principal feature analysis for spindle health diagnosis. Struct. Health Monit. 2011, 10, 631-642.
35. Jones, A.B. A general theory for elastically constrained ball and radial roller bearings under arbitrary load and speed conditions. ASME Trans. J. Basic Eng. 1960, 82, 309-320.
36. Altintas, Y.; Cao, Y. Virtual design and optimization of machine tool spindles. CIRP Ann. Manuf. Technol. 2005, 54, 379-382.
37. Cao, Y.Z.; Altintas, Y. A general method for the modeling of spindle-bearing systems. ASME Trans. J. Mech. Design. 2004, 126, 1089-1104.
38. Cao, Y.Z.; Altintas, Y. Modeling of spindle-bearing and machine tool systems for virtual simulation of milling operations. Int. J. Mach. Tool Manuf. 2007, 47, 1342-1350.
39. Cao, H.; Holkup, T.; Altintas, Y. A comparative study on the dynamics of high speed spindles with respect to different preload mechanisms. Int. J. Adv. Manuf. Tech. 2011, 57, 871-883.
40. Cao, H. Research on digital modeling theory of high-speed machinetool spindles and its application. Ph.D. Thesis, Xi'an Jiaotong University, Xi'an, China, 2010.
41. Harris, T.A. Rolling Bearing Analysis; John Wiley and Sons: New York, NY, USA, 1991.
42. Chang, L.C.; Lee, D.S. The development of a monitoring system using a wireless and powerless sensing node deployed inside a spindle. Sensors 2012, 12, 24-41.