# HHLP-for-weak-fault-feature-enhancement

**Repository Path**: hero-han/HHLP-for-weak-fault-feature-enhancement

## Basic Information

- **Project Name**: HHLP-for-weak-fault-feature-enhancement
- **Description**: Source codes for paper "Hierarchical hyper-Laplacian prior for weak fault feature enhancement"
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2020-08-24
- **Last Updated**: 2023-05-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# HHLP-for-weak-fault-feature-enhancement


This repository contains the implementation details of our paper: [ISA Transactions]
"[**Hierarchical hyper-Laplacian prior for weak fault feature enhancement**](https://doi.org/10.1016/j.isatra.2019.06.007)" 
by [Zhibin Zhao](https://zhaozhibin.github.io/). 


## About
Sparsity-assisted methods are one of the most effective fault feature extraction methods which have been widely studied recently. However, no one has explained or discussed the choice of a suitable sparse prior from the perspective of the probability theory. In this paper, we define a hierarchical hyper-Laplacian prior induced model (HHLP) through maximizing the posterior probability for bearing fault diagnosis. In the proposed model, we conclude that the hyper-Laplacian prior can better model coefficients of fault feature than the Laplacian prior. Furthermore, we introduce a hierarchical hyper-Laplacian prior which embeds the physical characteristics to discriminate the harmonic interference. The main insight of this paper is that we provide a new way to model the sparse prior from the perspective of maximizing the posterior probability.

## Dependencies
- Matlab R2016b
- [[TQWT—toolbox]](http://eeweb.poly.edu/iselesni/TQWT/index.html) from I. W. Selesnick. 

## Pakages

This repository is organized as:
- [funs](https://github.com/ZhaoZhibin/HHLP-for-weak-fault-feature-enhancement/tree/master/funs) contains the main functions of the algorithm.
- [util](https://github.com/ZhaoZhibin/HHLP-for-weak-fault-feature-enhancement/tree/master/util) contains the extra functions of the test.
- [Results](https://github.com/ZhaoZhibin/HHLP-for-weak-fault-feature-enhancement/tree/master/Results) contains the results of the algorithm.
- [data](https://github.com/ZhaoZhibin/HHLP-for-weak-fault-feature-enhancement/tree/master/data) contains the data generated by NSF I/UCR Center on Intelligent Maintenance Systems (IMS) with support from Rexnord Corp. in Milwaukee, WI.
- [tqwt_matlab_toolbox](https://github.com/ZhaoZhibin/HHLP-for-weak-fault-feature-enhancement/tree/master/tqwt_matlab_toolbox) contains the TQWT toolbox copied from I. W. Selesnick.
In our implementation, **Matlab R2016b** is used to perform all the experiments.

## Implementation:
Flow the steps presented below:
-  Clone this repository.
```
git clone https://github.com/ZhaoZhibin/HHLP-for-weak-fault-feature-enhancement.git
open it with matlab
```
-  Test Simulation: Check the parameters setting of simulation in `Config.m` and run `Test_simulaton.m`. 
-  Test Your Own Data: Run `Test_Your_Own_Data.m`. 


## Citation
If you feel our HHLP is useful for your research, please consider citing our paper: 

```
@article{zhao2019hierarchical,
  title={Hierarchical hyper-Laplacian prior for weak fault feature enhancement},
  author={Zhao, Zhibin and Wang, Shibin and An, Botao and Guo, Yanjie and Chen, Xuefeng},
  journal={ISA transactions},
  year={2019},
  publisher={Elsevier}
}
```
## Contact
- zhibinzhao1993@gmail.com