https://github.com/arundo/wids2019-equipment-ad
https://github.com/arundo/wids2019-equipment-ad
Last synced: 9 months ago
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- Host: GitHub
- URL: https://github.com/arundo/wids2019-equipment-ad
- Owner: arundo
- License: mit
- Created: 2019-03-21T08:37:06.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2022-09-23T22:24:55.000Z (over 3 years ago)
- Last Synced: 2025-04-11T11:52:04.383Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 5.32 MB
- Stars: 2
- Watchers: 6
- Forks: 4
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Build an Anomaly Detection Model using Deep Learning
**Workshop presented during Women in Data Science Conference 04.04.2019 in Oslo**
Original description is available at [WiDS pages](http://www.wids-oslo.org/).
The introductory slides are available in the [slides](/slides) folder of this repository.
## Setup
To reproduce the virtual environment used for the workshop install
[pipenv](https://pipenv.readthedocs.io/en/latest/) and type:
```bash
pipenv install
```
Then you should be able to start a jupyter notebook and execute the notebook content.
## Data
The data used in this workshop was extracted from the [Turbofan engine degradation simulation data set](https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/#turbofan) (dataset ID: "FD001").
Reference: Saxena, A., Goebel, K., Simon, D. and Eklund, N., 2008, October. Damage propagation modeling for aircraft engine run-to-failure simulation. In Prognostics and Health Management, 2008. PHM 2008. International Conference on (pp. 1-9). IEEE.
## Questions? Suggestions?
If you have any questions about the presented content or would like to suggest
ways we could improve this tutorial please reach out to us at
[support@arundo.com](mailto:support@arundo.com).