| ---
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| license: mit
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| tags:
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| - cbc-reference-model
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| - mlops-100-day
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| - predictive-maintenance
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| - pytorch
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| - lstm
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| ---
|
|
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| # CBC Reference Model: Turbofan Remaining Useful Life (C-MAPSS FD001)
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|
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| > Pre-trained reference model for the **CBC [MLOps 100-Day Track](https://github.com/careerbytecode/cbc-learning-hub/tree/main/100-days/mlops)** (Capstone 4). Published twin of ML Development Capstone 4. **This is the one deep-learning reference model** — it ships as a torch `state_dict` + `model.py`, NOT a single joblib.
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|
|
| ## Model details
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| - **Type:** single-layer LSTM (hidden 64) over a 30-cycle window of 15 normalized sensors -> scalar RUL (capped at 125).
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| - **Framework:** pytorch 2.12.0+cpu · **Serialization:** `manufacturing_lstm.pt` (state_dict) + `manufacturing_meta.joblib` (normalization + arch). Reconstruct with the shipped `model.py`.
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| - The LSTM is the rare counterpoint to the classical models: run-to-failure multivariate sensor sequences are the data shape deep learning is for. It beats an XGBoost baseline (RMSE 18.45) overall and decisively near failure.
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|
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| ## Intended use
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| Decision-support estimate of operational cycles remaining, to prioritize inspection/maintenance. NOT an automated ground-or-fly authority. Teaching/reference artifact.
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|
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| ## Training data
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| NASA C-MAPSS FD001 (100 train + 100 test run-to-failure engines, single operating condition). 15 non-flat sensors used. Simulated, no PII. NASA Open Data (public domain).
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|
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| ## Metrics (test = last cycle of each test engine vs RUL_FD001, scored once)
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| | Model | Test RMSE | Near-failure RUL[0,50) |
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| |---|---|---|
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| | XGBoost baseline | 18.45 | — |
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| | **LSTM (deployed)** | **14.88** | **4.78** |
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| The LSTM wins overall and is decisively better in the operationally critical near-failure band.
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|
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| ## How to load and predict
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| ```python
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| from huggingface_hub import snapshot_download
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| import sys, json
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| d = snapshot_download("careerbytecode/mlops-ref-manufacturing-rul")
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| sys.path.insert(0, d + "/model"); sys.path.insert(0, d)
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| from model import load_model, predict_rul # needs torch installed
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| model, meta = load_model(d + "/model")
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| sample = json.load(open(d + "/sample_input.json"))
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| print(predict_rul(model, meta, sample["window"])) # predicted RUL (cycles)
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| ```
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| **Serving requires `torch`** and the shipped `model.py` (the class definition) — a joblib load alone will not work.
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|
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| ## Limitations
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| - Trained on FD001 (one operating condition); FD002/FD004 (six conditions) need condition-aware normalization.
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| - RUL capped at 125: cannot distinguish a very-healthy from a merely-healthy engine, by design.
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| - Needs the full 30-cycle window + the training normalization stats to serve; a single reading is not enough. Simulated data — expect drift on real telemetry. Reference/teaching artifact only.
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|
|
| ---
|
| © 2015-2026 CareerByteCode. All rights reserved. | CC BY-NC-SA 4.0 (docs), MIT (code) | Authored by Raghavendra R, Platform Owner CareerByteCode, Solution Architect
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| |
