Source codes for the paper "Sparsity-assisted Fault Feature Enhancement: Algorithm-aware versus Model-aware"
This repository contains the implementation details of our paper: [TIM] "Sparsity-assisted Fault Feature Enhancement: Algorithm-aware versus Model-aware" by Zhibin Zhao.
Vibration signal analysis has become one of the important methods for machinery fault diagnosis. Extraction of weak fault features from vibration signals with heavy background noise remains a challenging problem. In this paper, we first introduce the idea of algorithm-aware sparsity-assisted methods for fault feature enhancement, which extends model-aware sparsity-assisted fault diagnosis and allows a more flexible and convenient algorithm design. In the framework of algorithm-aware methods, we define the generalized structured shrinkage operators and construct the generalized structured shrinkage algorithm (GSSA) to overcome the disadvantages of $ l_1 $-norm regularization based fault feature enhancement methods. We then perform a series of simulation studies and two experimental cases to verify the effectiveness of the proposed method. Additionally, comparisons with model-aware methods, including basis pursuit denoising and windowed-group-lasso, and fast kurtogram further verify the advantages of GSSA for weak fault feature enhancement.
- Matlab R2016b
- [TQWT—toolbox] from I. W. Selesnick.
This repository is organized as:
- Generalized-Structured-Shrinkage-Operators contains the main functions of the algorithm.
- utils contains the extra functions of the test.
- Performance_Data contains the performance comparison of different algorithms.
- results contains the results of the algorithm.
- Neighborhood_Thresholding contains the TQWT denoising methods using neighborhood thresholding.
- tqwt_matlab_toolbox contains the TQWT toolbox copied from I. W. Selesnick. In our implementation, Matlab R2016b is used to perform all the experiments.
Main functions:
- [Plot_Pure_Noise_Comparison.m] performs denoising when the features are submerged by Gaussian noise.
- [Plot_Noise_Plus_Harmonic_Interference_Comparison.m] performs denoising when the features are submerged by Gaussian noise and harmonic interference.
- [Plot_Penalty_Shrinkage.m] plots the penalties and their corresponding shrinkages.
- [Plot_performance_comparison.m] performs performance comparison of different algorithms.
Flow the steps presented below:
- Clone this repository.
git clone https://github.com/ZhaoZhibin/GSSA.git
open it with matlab
- Test Simulation with Gaussian noise: Run
Plot_Pure_Noise_Comparison.m. - Test Simulation with Gaussian noise and harmonic interference: Run
Plot_Noise_Plus_Harmonic_Interference_Comparison.m.
If you feel our GSSA is useful for your research, please consider citing our paper:
@article{zhao2020sparsity,
title={Sparsity-assisted Fault Feature Enhancement: Algorithm-aware versus Model-aware},
author={Zhao, Zhibin and Wang, Shibin and Xu, Weixin and Wu, Shuming and Wong David and Chen, Xuefeng},
journal={IEEE Transactions on Instrumentation and Measurement},
year={2020},
publisher={IEEE}
}