High-speed spindle fault diagnosis with the empirical mode decomposition and multiscale entropy method

Entropy

Volumn 17, Issue 4, 2015, Pages 2170-2183

  Hsieh, Nan Kai ^a   Lin, Wei Yen ^a   Young, Hong Tsu ^a  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

Author keywords

Ball bearing; Empirical mode decomposition (EMD); Fault diagnosis; Machine tool spindle; Multiscale entropy (MSE)

Indexed keywords

EID: 84930318795     PISSN: None     EISSN: 10994300     Source Type: Journal    
DOI: 10.3390/e17042170     Document Type: Article

References (35)

1. Xiong, X.; Yang, S.; Gan, C. A new procedure for extracting fault feature of multi-frequency signal from rotating machinery. Mech. Syst. Signal Process. 2012, 32, 306-319.
2. Tsao, W.-C.; Li, Y.-F.; D. D. Le; Pan, M.-C. An insight concept to select appropriate IMFs for envelope analysis of bearing fault diagnosis. Measurement 2012, 45, 1489-1498.
3. Saravanan, S.; Yadava, G.S.; Rao, P.V. Condition monitoring studies on spindle bearing of a lathe. Int. J. Adv. Manuf. Technol. 2006, 28, 993-1005.
4. Lei, Y.; He, Z.; Zi, Y. Application of the EEMD method to rotor fault diagnosis of rotating machinery. Mech. Syst. Signal Process. 2009, 23, 1327-1338.
5. Lei, Y.; Lin, J.; He, Z.; Zuo, M.J. A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mech. Syst. Signal Process. 2013, 35, 108-126.
6. Al-Hamdan, A. Effect of misalignment on the cutting force signature in drilling. J. Mater. Process. Technol. 2002, 124, 83-91.
7. Wu, T.Y.; Chen, J.C.; Wang, C.C. Characterization of gear faults in variable rotating speed using Hilbert-Huang Transform and instantaneous dimensionless frequency normalization. Mech. Syst. Signal Process. 2012, 30, 103-122.
8. Huang, N.E.; Shen, Z.; Long, S.R.; Wu, M.C.; Shih, H.H.; Zheng, Q.; Yuen, N.C.; Tung, C.C.; Liu, H.H. The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proc. R. Soc. Lond. 1998, 454A, 903-995.
9. Peng, Y. Empirical model decomposition based time-frequency analysis for the effective detection of tool breakage. J. Manuf. Sci. Eng. 2006, 128, 154-166.
10. Jiang, H.; Li, C.; Li, H. An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis. Mech. Syst. Signal Process. 2013, 36, 225-239.
11. Yu, D.; Cheng, J.; Yang, Y. Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings. Mech. Syst. Signal Process. 2005, 19, 259-270.
12. Cheng, J.; Yu, D.; Yu, Y. A fault diagnosis approach for roller bearings based on EMD method and AR model. Mech. Syst. Signal Process. 2006, 20, 350-362.
13. Rai, V.K.; Mohanty, A.R. Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform. Mech. Syst. Signal Process. 2007, 21, 2607-2615.
14. Peng, Z.K.; Tse, P.W.; Chu, F.L. A comparison study of improved Hilbert-Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing. Mech. Syst. Signal Process. 2005, 19, 974-988.
15. Lin, W.-Y.; Chuang, L.-C.; Young, H.-T. Condition-based shaft fault diagnosis with the empirical mode decomposition method. Proc. IMechE Part B: J. Eng. Manuf. 2010, 225, 723-734.
16. Pincus, S.M. Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. USA 1991, 88, 2297-2301.
17. Yan, R.; Gao, R.X. Approximate entropy as a diagnostic tool form machine health monitoring. Mech. Syst. Signal Process. 2007, 21, 824-839.
18. Ocak, H. Automatic detection of elileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst. Appl. 2009, 36, 2027-2036.
19. Richman, J.S.; Moorman, J.R. Physiological time-series analysis using approximate entropy and sample entropy. Am J. Physiol. Heart. Circ. Physiol. 2000, 278, H2039-H2049.
20. Costa, M.; Goldberger, A.L.; Peng, C.-K. Multiscale entropy analysis of complex physiologic time series. Phys. Rev. Lett. 2002, 89, 068102.
21. Zhang, L.; Xiong, G.; Liu, H.; Zou, H.; Guo, W. Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference. Expert Syst. Appl. 2010, 37, 6077-6085.
22. Lin, J.-L.; Liu, J.Y.-C.; Li, C.-W.; Tsai, L.F.; Chung, H.-Y. Motor shaft misalignment detection using multiscale entropy with wavelet denoising. Expert Syst. Appl. 2010, 37, 7200-7204.
23. Serrato, R.; Maru, M.M.; Padovese, L.R. Effect of lubricant viscosity grade on mechanical vibration of roller bearings. Tribol. Int. 2007, 40, 1270-1275.
24. Wilosn, J.S. Sensor Technology Handbook; Newnes: Oxford, UK, 2005.
25. Hansen, T.F. Accelerometer mounting techniques. B&K Web Course, June 2007.
26. Yang, Z.; Yu, Z.; Xie, C.; Huang, Y. Application of Hilbert-Huang transform to acoustic emission signal for burn feature extraction in surface grinding process. Measurement 2014, 47, 14-21.
27. Peng, Z.K.; Tse, P.W.; Chu, F.L. An improved Hilbert-Huang transform and its application in vibration signal analysis. J. Sound Vib. 2005, 286, 187-205.
28. Huang, N.E.; Shen, Z.; Long, S.R. A new view of nonlinear water waves: The Hilbert Spectrum. Annu. Rev. Fluid Mech. 1999, 31, 417-457.
29. Costa, M.; Goldberger, A.L.; Peng, C.-K. Multiscale entropy analysis of biological signals. Phys. Rev. E 2005, 71, 021906.
30. Wu, C.-H.; Kung, Y.-T. A parametric study on oil/air lubrication of a high-speed spindle. Precis. Eng. 2005, 29, 162-167.
31. Spiewak, S.A.; Nickel, T. Vibration based preload estimation in machine tool spindles. Int. J. Mach. Tools Manuf. 2001, 41, 567-588.
32. Saleem, A.; Diwakar, G.; Satyanarayana, M.R.S. Detection of unbalance in rotating machines using shaft deflection measurement during its operation. J. Mech. Civil Eng. 2012, 3, 8-20.
33. SKF-Catalogue 4950/IE, 2000.
34. 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. Technol. 2011, 57, 871-883.
35. Vibration Diagnostic Guide, SKF Reliability Systems, Application Note CM5003. Available online: www.budmgt.com/machanic/mcn-pdf/vibration-guide.pdf (accessed on 13 April 2015).