Advertisement

Wavelets pp 125-147 | Cite as

Wavelet Packet-Transform for Defect Severity Classification

  • Robert X. Gao
  • Ruqiang Yan
Chapter

Abstract

Once a defect is detected, the next question that comes up naturally is how severe the defect is. Since machine downtime is physically rooted in the progressive degradation of defects within the machine’s components, accurate assessment of the severity of defect is critically important in terms of providing input to adjusting the maintenance schedule and minimizing machine downtime. This chapter describes how wavelet packet transform (WPT)-based techniques can classify machine defect severity, with specific application to rolling bearings.

Keywords

Feature Vector Wavelet Packet Defect Severity Scatter Matrix Wavelet Packet Transform 
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. Altmann J, Mathew J (2001) Multiple band-pass autoregressive demodulation for rolling-element bearing fault diagnosis. Mech Syst Signal Process 15:963–977CrossRefGoogle Scholar
  2. Baydar N, Chen Q, Ball A, Kruger U (2001) Detection of incipient tooth defect in helical gears using multivariate statistics. Mech Syst Signal Process 15:303–321CrossRefGoogle Scholar
  3. De Boe P, Golinval JC (2003) Principal component analysis of a piezo-sensor array for damage localization. Int J Struct Health Monit 2(2):137–144CrossRefGoogle Scholar
  4. Duda R, Hart P, Stork D (2000) Pattern classification. Wiley-Interscience, New York.MATHGoogle Scholar
  5. Fan X, Zuo MJ (2006) Gearbox fault detection using Hilbert and wavelet packet transform. Mech Syst Signal Process 20:966–982CrossRefGoogle Scholar
  6. Fukunaga K (1990) Introduction to statistical pattern recognition, 2nd edn. Academic, New YorkMATHGoogle Scholar
  7. Gao R, Yan R (2007) Wavelet packet transform-based hybrid signal processing for machine health monitoring and diagnosis. In: The 6th international workshop on structural health monitoring, Stanford, CA, pp 598–605Google Scholar
  8. Goumas SK, Zervakis ME, Stavrakakis GS (2002) Classification of washing machine vibration signals using discrete wavelet analysis for feature extraction. IEEE Trans Instrum Meas 51(3):497–508CrossRefGoogle Scholar
  9. Haykin, S (1994) Neural networks. Macmillan Publishing Company, New YorkMATHGoogle Scholar
  10. He Q, Yan R, Kong F, Du R (2008) Machine condition monitoring using principle component representation. Mech Syst Signal Process. 23(2):446–466CrossRefGoogle Scholar
  11. Jack LB, Nandi AK (2001) Support vector machines for detection and characterization of rolling element bearing faults. Proc Inst Mech Eng 215:1065–1074CrossRefGoogle Scholar
  12. Jolliffe IT (1986) Principal component analysis. Springer-Verlag New York Inc, New YorkGoogle Scholar
  13. Kano M, Hasebe S, Hashimoto I (2001) A new multivariate statistical process monitoring method using principal component analysis. Comput Chem Eng 25:1103–1113CrossRefGoogle Scholar
  14. Kittler J (1975) Mathematical methods of feature selection in pattern recognition. Int J Man Mach Stud 7(5):609–637MathSciNetMATHCrossRefGoogle Scholar
  15. Lee BY, Tang YS (1999) Application of the discrete wavelet transform to the monitoring of tool failure in end milling using the spindle motor current. Int J Adv Manuf Technol 15(4):238–243CrossRefGoogle Scholar
  16. Li B, Chow M, Tipsuwan Y, Hung JC (2000a) Neural-network-based motor rolling bearing fault diagnosis. IEEE Trans Ind Electron 47(5):1060–1069CrossRefGoogle Scholar
  17. Li XL, Tso SK, Wang J (2000b) Real-time tool condition monitoring using wavelet transforms and fuzzy techniques. IEEE Trans Syst Man Cybern C Appl Rev 30(3):352–357CrossRefGoogle Scholar
  18. Liu B, Ling SF, Meng Q (1997) Machinery diagnosis based on wavelet packets. J Vib Control 3:5–17CrossRefGoogle Scholar
  19. Maki Y, Loparo KA (1997) A neural-network approach to fault detection and diagnosis in industrial processes. IEEE Trans Control Syst Technol 5(6):529–541CrossRefGoogle Scholar
  20. Malhi A, Gao R. (2004) PCA-based feature selection scheme for machine defect classification. IEEE Trans Instrum Meas 53(6):1517–1525CrossRefGoogle Scholar
  21. McCormick AC, Nandi AK (1997) Classification of the rotating machine condition using artificial neural networks. Proc Inst Mech Eng C 211:439–450CrossRefGoogle Scholar
  22. 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 195:162–168CrossRefGoogle Scholar
  23. Paya BA, Esat II, Badi MNM (1997) Artificial neural network based fault diagnosis of rotating machinery using wavelet transforms as a preprocessor. Mech Syst Signal Process 11(5):751–765CrossRefGoogle Scholar
  24. Prabhakar S, Mohanty AR, Sekhar AS (2002) Application of discrete wavelet transform for detection of ball bearing race faults. Tribol Int 35(12):793–800CrossRefGoogle Scholar
  25. Yan R, Gao R (2004) Harmonic wavelet packet transform for on-line system health diagnosis. SPIE international symposium on sensors and smart structures technologies for civil, mechanical and aerospace systems, San Diego, CA, pp 512–522Google Scholar
  26. Yen G, Lin K (2000) Wavelet packet feature extraction for vibration monitoring. IEEE Trans Ind Electron 47(3):650–667CrossRefGoogle 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