This repository contains the implementation details of our paper: [Journal of Sound and Vibration] "A weighted multi-scale dictionary learning model and its applications on bearing fault diagnosis" by Zhibin Zhao.
Extracting impulsive information under strong background noise and harmonic interference is a challenging problem for bearing fault diagnosis. Multi-scale transforms have achieved great success in extracting impulsive feature information, however, how to choose a suitable transform is a difficult problem, especially in the case of strong noise interference. Therefore, dictionary learning methods have attracted more and more attention in recent years. A weighted multi-scale dictionary learning model (WMSDL) is proposed in this paper which integrates the multi-scale transform and fault information into a unified dictionary learning model and it successfully overcomes four disadvantages of traditional dictionary learning algorithms including lacking the multi-scale property; restricting training samples to local patches; being sensitive to strong harmonic interference; suffering from high computational complexity. Moreover, algorithmic derivation, computational complexity and parameter selection are discussed. Finally, The effectiveness of the proposed method is verified by both the numerical simulations and experiments. Comparisons with other state-of-the-art methods further demonstrate the superiority of the proposed method.
- Matlab R2016b
- [TQWT—toolbox] from I. W. Selesnick.
- [ksvdbox13] from Dr. Ron Rubinstein.
- [ompbox10] from Dr. Ron Rubinstein.
This repository is organized as:
- funs contains the main functions of the algorithm.
- util contains the extra functions of the test.
- Results contains the results of the algorithm.
- tqwt_matlab_toolbox contains the TQWT toolbox copied from I. W. Selesnick.
- ksvdbox13 contains the KSVD toolbox copied from Dr. Ron Rubinstein.
- ompbox10 contains the OMP toolbox copied from Dr. Ron Rubinstein. In our implementation, Matlab R2016b is used to perform all the experiments.
Flow the steps presented below:
- Clone this repository.
git clone https://github.com/ZhaoZhibin/Weighted_Multi-Scale_Dictionary_Learning.git
open it with matlab
- Test Simulation: Check the parameters setting of simulation in
Config.mand runTest_simulaton.m.
If you feel our WMSDL is useful for your research, please consider citing our paper:
@article{zhao2019weighted,
title={A weighted multi-scale dictionary learning model and its applications on bearing fault diagnosis},
author={Zhao, Zhibin and Qiao, Baijie and Wang, Shibin and Shen, Zhixian and Chen, Xuefeng},
journal={Journal of Sound and Vibration},
volume={446},
pages={429--452},
year={2019},
publisher={Elsevier}
}