Skip to main content
Article thumbnail
Location of Repository

Novel Acoustic Emission Signal Processing Methods for Bearing Condition Monitoring

By Yanhui Feng


Rolling Element Bearing is one of the most common mechanical components to be found in critical industrial rotating machinery. Since the failure of bearings will cause the machine to malfunction and may quickly lead to catastrophic failure of the machinery, it is very important to detect any bearing deterioration at an early stage. In this thesis, novel signal processing methods based on Acoustic Emission measurement are developed for bearing condition monitoring. The effectiveness of the proposed methods is experimentally demonstrated to detect and diagnose localised defects and incipient faults of rolling element bearings on a class of industrial rotating machinery – the iGX dry vacuum pump. Based on the cyclostationary signal model and probability law governing the interval distribution, the thesis proposes a simple signal processing method named LocMax-Interval on Acoustic Emission signals to detect a localised bearing defect. By examining whether the interval distribution is regular, a localised defect can be detected without a priori knowledge of shaft speed and bearing geometry. The Un-decimated Discrete Wavelet Transform denoising is then introduced as a pre-processing approach to improve the effective parameter range and the diagnostic capability of the LocMax-Interval method. The thesis also introduces Wavelet Packet quantifiers as a new tool for bearing fault detection and diagnosis. The quantifiers construct a quantitative time-frequency analysis of Acoustic Emission signals. The Bayesian method is studied to analyse and evaluate the performance of the quantifiers. This quantitative study method is also performed to investigate the relationships between the performance of the quantifiers and the factors which are important in implementation, including the wavelet order, length of signal segment and dimensionality of diagnostic scheme. The results of study provide useful directions for real-time implementation

Publisher: University of Leicester
Year: 2008
OAI identifier:

Suggested articles


  1. (2004a) “State of the Art in Monitoring Rotating Machinery: Part 1,” Sound and Vibration.
  2. (2004a) “Unsupervised Noise Cancellation for Vibration Signals: Part I – doi
  3. (2004b) “State of the Art in Monitoring Rotating Machinery: Part 2,” Sound Bibliography and Vibration.
  4. (2004b) “Unsupervised Noise Cancellation for Vibration Signals: Part doi
  5. (2009). A Neuro-fuzzy Technique for Fault Diagnosis and Its Application to Rotating Machinery,“ Reliability Engineering and System Safety, doi
  6. (1999). A Review of Vibration and Acoustic Measurement Methods for the Detection of Defects in doi
  7. (2006). A Roller Bearing Fault Diagnosis Method based on Bibliography EMD Energy Entropy and ANN, doi
  8. (2003). A Stochastic Model for Simulation and Diagnostics of Rolling Element Bearings with Localized Faults,” doi
  9. (1989). A Theory for Multiresolution Signal Decomposition: The Wavelet Representation,” doi
  10. (2007). A Wavelet Cluster-based Band-pass Filtering and Envelope Demodulation Approach with Application to Fault Diagnosis in a Dry Vacuum Pump”, doi
  11. (2000). Acoustic Emission of Rolling Bearing Lubricated with doi
  12. (1995). Adapting to Unknown Smoothness via Wavelet Shrinkage,” doi
  13. (1987). An introduction to acoustic emission,” doi
  14. (1992). An Introduction to Wavelets, doi
  15. (2000). Application of Acoustic Emission Technique for the Detection of Defects in doi
  16. (2000). Application of Synchronous Averaging to Vibration Monitoring of Rolling doi
  17. (2004). Application of Wavelet Transform doi
  18. (2003). Aritificial Neural Network Based Fault Diagnostics of Rolling Element doi
  19. (1997). Artificial Neural Network Based Fault Diagnostics of Rotating Machinery using Wavelet Transforms as A Preprocessor, doi
  20. (2007). Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing,“ doi
  21. (2003). Basic Vibration Signal Processing for Bearing Fault Bibliography Detection”, doi
  22. (2004). Bearing fault diagnosis based on wavelet transform and fuzzy inference, doi
  23. (2004). Bearing Fault Diagnosis using doi
  24. (1996). Comparison of artificial neural networks and other statistical methods for rotating machine condition classification", doi
  25. (2007). Cyclic Spectral Analysis of Rolling Element Bearing Signals: Facts and Frictions,” doi
  26. (2006). Cyclostationarity: Half a doi
  27. (2004). Defect Detection for Bearings Using Envelope Spectra of Wavelet Transform,” doi
  28. (2005). Design of Mixture Denoising for Detecting Fault Bearing Signals,” doi
  29. (2006). Development of Acoustic Emission Technology for Condition Monitoring and Diagnosis of Rotating Machines: Bearings, Pumps, Gearboxes, Engines and Rotating Structures,” The Shock and Vibration Digest. doi
  30. (2007). Diagnostics, Prognostics, and Fault Simulation for Rolling Element Bearings,” Ph.D. Thesis,
  31. (2002). Differential Diagnosis of Gear and Bearing Faults,” doi
  32. (2006). Discrete Wavelet-based Thresholding study on Acoustic Emission Signals to Detect Bearing Defect on A Rotating
  33. (2007). Discriminating Contamination Fault and Localised Bearing Defect using Acoustic Emission Signal,”
  34. (2007). Evolving an Artificial Neural Network Classifier for Condition doi
  35. (2008). Expert System Development for Vibration Analysis doi
  36. (2005). Fault Detection using Genetic Programming, doi
  37. (2003). Fault diagnosis system for rotary machine based on fuzzy neural networks,“ doi
  38. (2005). Feature Separation using ICA for a One-dimensional Time Series and Its Application in Fault Detection,” doi
  39. (1994). Ideal Spatial Adaptation via doi
  40. (2002). Image Denoising using Wavelets and Spatial Context Modeling,” Ph.D. Thesis,
  41. (2007). Improving Detectability on Localised Bearing Defect using Acoustic Emission Signal,”
  42. (2007). Improving Detectability on Localized Bearing Defect using Acoustic Emission Signal,”
  43. (2007). Intelligent condition monitoring of a gearbox using artificial neural network,“ doi
  44. (2004). Mechanical Fault Detection Based on the Wavelet De-Noising Technique,” doi
  45. (1986). Mechanical Signature Analysis: theory and application, doi
  46. (1984). Model for the Vibration Produced by A Single Point Defect in A Rolling Element Bearing,” doi
  47. (2001). Modern vacuum practice
  48. (2001). Multiple Band-pass Autoregressive Demodulation for Rolling Element Bearing Fault doi
  49. (2004). Noninvasive Determination of Fetal Heart Rate and Short Term Heart Rate Variability using Solely Doppler Ultrasound with Autocorrelation,”
  50. (2008). Normalised Wavelet Packets Quantifiers for Condition Monitoring,” Mechanical Systems and Signal Processing, doi
  51. (1961). On measures of entropy and information,” doi
  52. (1975). On the Autocorrelation of One doi
  53. (2000). Optimisation of Bearing Diagnostic Techniques using doi
  54. (2006). Processing Acoustic emission signals from dry vacuum pumps for fault diagnostics using wavelets”
  55. (2007). Rényi Entropy-based Generalized Statistical Moments for Early Fatigue Defect Detection of Rolling-Element Bearing,” doi
  56. (2007). Review: Support Vector Machine doi
  57. (2002). Rolling Element Bearing Fault Diagnosis using Wavelet Packets,” doi
  58. (1992). Signal Interception: Performance Advantages of Cyclic-Feature Detectors,” doi
  59. (2002). Singularity Analysis using Continuous Wavelet Transform for Bearing Fault doi
  60. (1992). Singularity Detection and Processing with Wavelets,” doi
  61. (2007). Study of Solid Contamination doi
  62. (1992). Ten Lectures on Wavelets, doi
  63. (1959). The Mathematical Theory of doi
  64. (2001). The Relationship between Spectral Correlation and Envelope Analysis doi
  65. (2006). The Spectral Kurtosis: A Useful Tool for Characterising Non-stationary Signals,” doi
  66. (2006). The Spectral Kurtosis: Application to the Vibratory Surveillance and doi
  67. (1984). The Vibration Monitoring of Rolling Element Bearings by the High-frequency Resonance Technique: doi
  68. (2002). Third-order Spectral Techniques for the Diagnosis of Motor Bearing Condition using Artificial Neural Networks, doi
  69. (1998). Time-frequency analysis of electroencephalogram series. III. Wavelet packets and information cost function,” Physical Review E, doi
  70. (2001). Wavelet Analysis and Envelope Detection for Rolling Element Bearing Fault Diagnosis - Their Effectiveness and Flexibilities,” doi
  71. (2001). Wavelet entropy: A New Tool for Analysis of doi
  72. (2006). Wavelet Filter-based Weak Signature Detection Method and Its Application on Rolling Bearing Prognostics,” doi
  73. (2000). Wavelet Methods for Time Series Analysis, doi
  74. (2000). Wavelet Toolbox- for use with MATLAB, doi
  75. (1996). Wavelets and Filter Banks. doi
  76. (1991). Wavelets and Signal Processing”, doi
  77. (1995). Wavelets and Subband Coding. doi
  78. (1996). Wavelets: What next?,” doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.