| | --- |
| | title: Engine Predictive Maintenance App |
| | emoji: "🛠️" |
| | colorFrom: purple |
| | colorTo: pink |
| | sdk: docker |
| | pinned: false |
| | --- |
| | |
| | # 🛠️ Smart Engine Predictive Maintenance App |
| |
|
| | This interactive Streamlit application predicts whether an engine is likely to be **Faulty (1)** or **Normal (0)** using real-time sensor readings. |
| | It is designed to support **preventive maintenance decision-making** by identifying engines at higher risk of failure before breakdown occurs. |
| |
|
| | --- |
| |
|
| | ## ✅ Key Features |
| |
|
| | - **Single Engine Prediction** using manual sensor inputs |
| | - **Probability-based output** for Faulty / Normal (where supported by the model) |
| | - **Feature engineering built-in** (the app automatically computes engineered features to match the training schema) |
| | - **Download engineered input row** as CSV for traceability |
| | - **Bulk CSV Prediction** (upload a CSV and generate batch predictions) |
| | - **Download bulk predictions** directly from the UI |
| |
|
| | --- |
| |
|
| | ## 🧠 Model Details |
| |
|
| | - **Algorithm:** Gradient Boosting Classifier |
| | - **Training Data:** Engine sensor telemetry dataset |
| | - **Target Variable:** `Engine Condition` |
| | - `0 = Normal` |
| | - `1 = Faulty` |
| |
|
| | **Reference Metrics (from model evaluation):** |
| | - Recall (Faulty): ~0.84 |
| | - ROC-AUC: ~0.70 |
| | - PR-AUC: ~0.80 |
| |
|
| | --- |
| |
|
| | ## 🧾 Required Input Features (Single & Bulk) |
| |
|
| | Your CSV or manual inputs must include **only the raw sensor columns** below: |
| |
|
| | 1. `Engine rpm` |
| | 2. `Lub oil pressure` |
| | 3. `Fuel pressure` |
| | 4. `Coolant pressure` |
| | 5. `lub oil temp` |
| | 6. `Coolant temp` |
| |
|
| | The app computes additional engineered features internally (ratios, indices, and warning flags) to align with the model training pipeline. |
| |
|
| | --- |
| |
|
| | ## 📦 Bulk Prediction Instructions |
| |
|
| | 1. Upload a CSV file with the 6 required raw sensor columns listed above. |
| | 2. The app will generate: |
| | - `Predicted_Class` (0/1) |
| | - `Faulty_Probability` (if available) |
| |
|
| | 3. Download the results using the provided **Download Bulk Predictions CSV** button. |
| |
|
| | --- |
| |
|
| | ## 🚀 Deployment |
| |
|
| | This Space uses a Docker-based deployment with Streamlit running on port **8501**. Hugging Face automatically maps ports during deployment. |
| |
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| | --- |
| |
|
| | ## 🔗 Project Links |
| |
|
| | - **Model Hub:** `simnid/predictive-maintenance-model` |
| | - **Dataset Hub:** `simnid/predictive-engine-maintenance-dataset` |
| | - **GitHub Repository:** *(add your repo link here once finalized)* |
| |
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| | --- |
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