insdout/Time-Series-Hybrid-Autoencoder
Remaining Useful Life estimation and sensor data generation by VAE and diffusion model on C-MAPSS dataset.
This project helps maintenance engineers and operations managers predict when industrial equipment, like aircraft engines, will need servicing or replacement. It takes sensor data from operating machinery and outputs an estimated 'Remaining Useful Life' (RUL) for that equipment. Additionally, it can generate realistic synthetic sensor data, useful for testing or training other predictive models, without needing to run actual machines to failure.
No commits in the last 6 months.
Use this if you need to accurately forecast the degradation of complex machinery based on its operational sensor data and want to generate synthetic data for further analysis or model development.
Not ideal if you are looking for a simple, off-the-shelf maintenance scheduling tool that doesn't involve working with raw sensor data or machine learning models.
Stars
49
Forks
2
Language
Python
License
MIT
Category
Last pushed
May 17, 2024
Commits (30d)
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