Advertisement

Wavelets pp 103-124 | Cite as

Wavelet Integrated with Fourier Transform: A Unified Technique

  • Robert X. Gao
  • Ruqiang Yan
Chapter

Abstract

Fourier transform-based spectral analysis has been widely applied to processing signals, such as vibration and acoustic signals (Mori et al. 1996; Tandon and Choudhury 1999; Cavacece and Introini 2002), acquired from manufacturing systems. Because of noise contamination and signal interference, the constituent components of interest may be submerged in the signal and difficult to be revealed through a spectral analysis (Ho and Randall 2000).

Keywords

Wavelet Coefficient Decomposition Level Rolling Element Defect Feature Radial Load 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Bracewell, R (1999) The Fourier transform and its applications, 3rd edn. McGraw-Hill, New YorkGoogle Scholar
  2. Byrne G, O’Donnell GE (2007) An integrated force sensor solution for process monitoring of drilling operations. CIRP Ann Manuf Technol 56(1):89–92CrossRefGoogle Scholar
  3. Cavacece M, Introini A (2002) Analysis of damage of ball bearings of aeronautical transmissions by auto-power spectrum and cross-power spectrum. ASME J Vib Acoust 124(2):180–185CrossRefGoogle Scholar
  4. Daubechies I (1992) Ten lectures on wavelets. SIAM, Philadelphia, PAMATHCrossRefGoogle Scholar
  5. Gao R, Yan R (2006) Non-stationary signal processing for bearing health monitoring. Int J Manuf Res 1(1):18–40CrossRefGoogle Scholar
  6. Ge M, Du, R, Zhang GC, Xu YS (2004) Fault diagnosis using support vector machine with an application in sheet metal stamping operations. Mech Syst Signal Process 18(1):143–159CrossRefGoogle Scholar
  7. Gibson J (1999) Principle of digital and analog communication, 2nd edn. Prentice Hall, Inc, Upper Saddle River, NJGoogle Scholar
  8. Harris TA (1991) Rolling bearing analysis, 3rd edn. Wiley, New YorkGoogle Scholar
  9. Ho D, Randall RB (2000) Optimization of bearing diagnostic techniques using simulated and actual bearing fault signals. Mech Syst Signal Process, 14(5):763–788CrossRefGoogle Scholar
  10. Holm-Hansen BT, Gao R, Zhang L (2004) Customized wavelet for bearing defect detection. ASME J Dyn Syst Meas Control 126(6):740–745CrossRefGoogle Scholar
  11. Kaiser G (1994) A friendly guide to wavelets. Birkhäuser, Boston, MAMATHGoogle Scholar
  12. Malekian M, Park SS, Jun M (2009) Tool wear monitoring of micro-milling operations. J Mater Process Technol 209:4903–4914CrossRefGoogle Scholar
  13. Mori K, Kasashima N, Yoshioka T, Ueno Y (1996) Prediction of spalling on a ball bearing by applying the discrete wavelet transform to vibration signals. Wear 195(1–2):162–168CrossRefGoogle Scholar
  14. Obikawa T, Shinozuka J (2004) Monitoring of flank wear of coated tools in high speed machining with a neural network ART2. Int J Mach Tools Manuf 44:1311–1318CrossRefGoogle Scholar
  15. Orhan S, Aktürk N, Çelik V (2006) Vibration monitoring for defect diagnosis of rolling element bearings as a predictive maintenance tool: comprehensive case studies. NDTE Int 39:293–298CrossRefGoogle Scholar
  16. SKF Company (1996) SKF bearing maintenance handbook. SKF Company, DenmarkGoogle Scholar
  17. Tandon T, Choudhury A (1999) A review of vibration and acoustic measurement methods for the defection of defects in rolling element bearings. Tribol Int 32:469–480CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringUniversity of ConnecticutStorrsUSA
  2. 2.School of Instrument Science and EngineeringSoutheast UniversityNanjingChina, People’s Republic

Personalised recommendations