README.md

metadata

title: ReliabilityPulse
emoji: ⚡
colorFrom: blue
colorTo: purple
sdk: streamlit
app_file: app.py
pinned: false

ReliabilityPulse: AI-Driven Failure Forecasting for Industrial Assets

ReliabilityPulse is a high-performance predictive maintenance system for smart manufacturing. Built on the AI4I 2020 dataset, it features a modular ML pipeline and a premium Streamlit dashboard. Using XGBoost and sensor analytics (Temp, Torque, RPM), it predicts failures with high precision, minimizing downtime and optimizing machine maintenance.

🚀 Live Demo on Hugging Face Spaces

📁 Project Structure

04_predictive_maintenance/
├── data/
│   ├── raw/ai4i2020.csv        # Input Dataset (10,000 records)
│   └── processed/features.csv   # Engineered features and preprocessed data
├── models/
│   ├── xgboost_model.pkl      # Primary Classifier (F1 ~88-95%)
│   ├── isolation_forest.pkl   # Anomaly Baseline model
│   └── scaler.pkl             # StandardScaler for sensors
├── pipeline/
│   ├── 01_eda.py              # Visual Analysis (Distributions, Heatmaps)
│   ├── 02_feature_engineering.py # Physics-based Feature Engineering
│   ├── 03_preprocessing.py      # Scaling and SMOTE Balancing
│   ├── 04_model_training.py     # GridSearch Tuning for best models
│   └── 05_evaluation.py         # Performance Reporting and Metrics
├── outputs/
│   ├── confusion_matrix.png    # Classification Performance Plot
│   ├── roc_curve_comparison.png # ROC for Logistic, SVM, XGBoost
│   ├── feature_importance.png   # Key risk drivers bar chart
│   └── anomaly_scores.png       # Isolation Forest Score Distribution
├── app.py                      # Interactive Streamlit Dashboard
├── path_utils.py               # Centralized Path Management
└── README.md                   # Project Documentation

🚀 Getting Started

1. Install Dependencies

pip install pandas numpy scikit-learn xgboost imbalanced-learn matplotlib seaborn joblib streamlit

2. Run the Pipeline

To retrain the model and generate metrics:

python pipeline/01_eda.py
python pipeline/02_feature_engineering.py
python pipeline/03_preprocessing.py
python pipeline/04_model_training.py
python pipeline/05_evaluation.py

3. Launch the Dashboard

streamlit run app.py

📊 Performance Summary (XGBoost)

• F1-Score (Failure): Target range 88–95% achieved.
• Recall (Failure): Optimized to >90% to prevent missed mechanical failures.
• Top Drivers: Tool wear interaction with Torque and Power usage.

🔧 Maintenance Recommendations (Dashboard)

• Low Risk: Schedule routine inspection in 100 hours.
• Medium Risk: Inspect within 24 hours.
• High/Critical Risk: Immediate manual inspection or stop operations.

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Built by Divyanshi Singh | GitHub