Data-driven adaptive chirp mode decomposition

A data-driven adaptive signal decomposition method for time-varying AM-FM signals

The matlab codes permit to reproduce some results in the paper: Hongbing Wang, Shiqian Chen, et al. Data-driven adaptive chirp mode decomposition with application to machine fault diagnosis under non-stationary conditions, Mechanical Systems and Signal Processing, 2022. The algorithm used in the paper is an improved version of that in the paper: Shiqian Chen, et al. Adaptive chirp mode pursuit: Algorithm and applications, Mechanical Systems and Signal Processing, 2018. Some of the scripts are adopted from the paper: Shiqian Chen, et al. Nonlinear Chirp Mode Decomposition: A Variational Method, IEEE Transactions on Signal Processing, 2017. and the paper: Shiqian Chen, et al. Detection of rub-impact fault for rotor-stator systems: A novel method based on adaptive chirp mode decomposition, Journal of Sound and Vibration, 2019.

Copyright (c) belongs to the authors of the papers. An acknowledgment for the codes and the citations about the papers above should be included in the publications as long as the codes are used.

Tips for users:

The function DDACMD.mrequires a low-pass filter. The low-pass filter (lowpass.m) of MATLAB has good filtering performance but earlier versions than MATLAB_2018 do not include lowpass.m. Therefore, we give two editions of the codes for DDACMD. The edition for MATLAB_2018 and later versions applies lowpass.mof MATLAB to realize low-pass filtering. The edition for earlier versions than MATLAB_2018 applies low_filter.m developed by the authors to realize low-pass filtering.

Contact Information:

Dr. Shiqian Chen

State Key Laboratory of Traction Power

Southwest Jiaotong University

Chengdu, 610031

P.R. China

Email: [e-mail]

ResearchGate: https://www.researchgate.net/profile/Shiqian-Chen-2