README.md

metadata

title: CMAPSS Reliability Dashboard
emoji: 🛩️
colorFrom: blue
colorTo: green
sdk: docker
app_port: 7860
pinned: false
license: mit

CMAPSS Reliability & RUL Dashboard

Interactive dashboard for the NASA CMAPSS turbofan-degradation dataset (subsets FD001, FD002, FD004). For each subset:

• Operational-regime clustering (KMeans, k=1 for FD001, k=6 for FD002/FD004)
• Per-regime sensor normalization
• Weibull fleet model (β, η, MTTF), with 2-component mixture overlay on FD004
• CatBoost RUL prediction (piecewise target capped at 150 cycles)

Local run with uv

uv sync
uv run python app.py

Files

• app.py — Gradio dashboard
• rul_multi.py — feature engineering, regime clustering, normalization
• weibull_multi.py — 2-component Weibull EM mixture fit
• data/ — 9 CMAPSS files (train/test/RUL × 3 subsets)
• cache/ — pre-trained CatBoost models, one per subset
• Dockerfile — uv-based image for HF Spaces

Deployment to Hugging Face Spaces

1. Generate the lockfile locally (one-shot):

   uv lock
   

2. Create a new Space on huggingface.co (SDK = Docker), clone it, copy every file from this directory into the clone, then push:

   git lfs install
   git lfs track "data/*.txt" "cache/*.cbm"
   git add .gitattributes
   git add .
   git commit -m "Initial deploy"
   git push
   

   The .txt and .cbm files are tracked via Git LFS to keep the repo light. The Space will build the Docker image and start the app on port 7860.

Model card

Subset	Weibull β	Weibull η	Test RMSE	Test NASA
FD001	4.55	225	~16.7	~570
FD002	4.48	229	~27.0	~8 600
FD004	3.23	278	~27.6	~7 800