https://github.com/bagustris/vbl-va001
Lab-scale Vibration Analysis Dataset and Its Machine Learning Methods
Science Score: 49.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 1 DOI reference(s) in README -
✓Academic publication links
Links to: arxiv.org, zenodo.org -
○Committers with academic emails
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (7.5%) to scientific vocabulary
Repository
Lab-scale Vibration Analysis Dataset and Its Machine Learning Methods
Basic Info
- Host: GitHub
- Owner: bagustris
- License: mit
- Language: Python
- Default Branch: master
- Size: 1000 KB
Statistics
- Stars: 25
- Watchers: 1
- Forks: 5
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
VBL-V001
Baseline methods for the paper Lab-scale Vibration Analysis Dataset and Baseline Methods for Machinery Fault Diagnosis with Machine Learning.
Dataset
Download from here: https://zenodo.org/record/7006575#.Y3W9lzPP2og.
Locate the dataset in a path like /data/VBL-VA001.
Structure of dataset:
```bash
bagus@m049:VBL-VA001$ tree -L 2 . --filelimit 100
.
├── bearing [1000 entries exceeds filelimit, not opening dir]
├── misalignment [1000 entries exceeds filelimit, not opening dir]
├── normal [1000 entries exceeds filelimit, not opening dir]
└── unbalance [1000 entries exceeds filelimit, not opening dir]
4 directories, 4000 files ```
You can also try the extracted feature under data directory and run
the following codes.
Running the program
```bash
First, extract the feature
$ python3 extract_feature.py
Then you can run any train_* program, i.e.,:
$ python3 train_svm.py Shape of Train Data : (3200, 27) Shape of Test Data : (800, 27) Optimal C: 69 Max test accuracy: 1.0 ```
Note on BPFO/BPFI
The BPFO and BPFI values are obtained from the pump bearing type datasheet, namely type NTN Bearing 6201, which has a BPFO coefficient of 2.62 and a BPFI coefficient of 4.38.
Citation (Bibtex)
bibtex
@ARTICLE{Atmaja2023,
author = {Atmaja, Bagus Tris and Ihsannur, Haris and Suyanto and Arifianto, Dhany},
title = {Lab-Scale Vibration Analysis Dataset and Baseline Methods for Machinery Fault Diagnosis with Machine Learning},
year = {2023},
journal = {Journal of Vibration Engineering and Technologies},
doi = {10.1007/s42417-023-00959-9},
type = {Article},
publication_stage = {Article in press},
source = {Scopus},
}
Owner
- Name: Bagus Tris Atmaja
- Login: bagustris
- Kind: user
- Location: Tsukuba
- Company: AIST
- Website: http://www.bagustris.blogspot.com
- Twitter: btatmaja
- Repositories: 221
- Profile: https://github.com/bagustris
Researcher @aistairc @VibrasticLab
GitHub Events
Total
- Watch event: 7
- Push event: 1
- Fork event: 3
Last Year
- Watch event: 7
- Push event: 1
- Fork event: 3
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Bagus Tris Atmaja | b****s@y****m | 24 |
Issues and Pull Requests
Last synced: 10 months ago
All Time
- Total issues: 0
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 0
- Total pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0