Machine learning toolbox for chatter detection with Wavelet Packet Transform (WPT) and Ensemble Empirical Mode Decomposition (EEMD)

This toolbox includes the documentation for the Python codes that extract features by using Wavelet Packet Transform (WPT) and Ensemble Empirical Mode Decomposition (EEMD) and diagnose chatter in turning process for different cutting configurations. Algorithms are based on the methods explained in [Yesilli2019]. The experimental data in both raw and processed format can be found in Mendeley repository [Khasawneh2019]. Python and MATLAB codes are available in GitHub repository.

Note: Please read the instructions in MATLAB Codes for Wavelet Packet Transform before using the Python codes for Wavelet Packet Transform (WPT).

Contents

• MATLAB Codes for Wavelet Packet Transform
  • Wavelet packet numbering in MATLAB
• Wavelet Packet Transform (WPT)
  • Feature extraction and supervised classification using WPT
  • Transfer Learning Application Using WPT
  • Transfer Learning On Two Cases Using WPT
• Ensemble Empirical Mode Decomposition (EEMD)
  • Feature extraction and supervised classification using EEMD
  • Transfer Learning Application Using EEMD
  • Transfer Learning On Two Cases Using EEMD

References

[1]	F.A. Khasawneh, A. Otto, and M.C. Yesilli. Turning dataset for chatter diagnosis using machine learning. Mendeley Data, v1. http://dx.doi.org/10.17632/hvm4wh3jzx.1, 2019. doi:10.17632/hvm4wh3jzx.1.

[2]	Melih C. Yesilli, Firas A. Khasawneh, and Andreas Otto. On transfer learning for chatter detection in turning using wavelet packet transform and ensemble empirical mode decomposition. CIRP Journal of Manufacturing Science and Technology, 2019. doi:10.1016/j.cirpj.2019.11.003.

• Index
• Module Index
• Search Page