🛠 Equipment Failure Dashboard with Predictive Maintenance

This project is a Streamlit web application that provides interactive data analysis and predictive maintenance insights for equipment failure management. It helps identify failure-prone models and components, perform root cause analysis using Apriori algorithm, and predict future equipment failures using Random Survival Forest models.

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📌 Features

🔍 Data Analysis

• Upload equipment failure CSV files
• Filter data by model numbers and part names
• Visualize top models and parts by failure rate
• Root Cause Analysis using Apriori Algorithm for association rules

🔧 Predictive Maintenance

• Predict "Time to Failure" for equipment using Random Survival Forest
• Calculate Failure Risk Scores
• Analyze high-risk spare parts
• Visualize failure time distribution and risk score comparisons
• Suggest spare part optimization based on risk level

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🚀 Tech Stack & Tools

• Frontend: Streamlit
• Visualization: Plotly, Matplotlib, Seaborn
• Data Manipulation: Pandas, NumPy
• Machine Learning:
  • Association Rules: mlxtend
  • Predictive Modeling: sksurv (scikit-survival)
• Modeling: Random Survival Forests

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📂 Folder Structure

stulz-proj/ │ ├── app.py # Main Streamlit Application ├── requirements.txt # Required dependencies └── sample_data.csv # Example CSV data

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⚙️ How to Run Locally

1. Clone the Repository
git clone https://github.com/your-username/stulz-proj.git
cd stulz-proj

2. Create a Virtual Environment
bash
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python -m venv venv
venv\Scripts\activate  # On Windows

3. Install Dependencies
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pip install --no-cache-dir -r requirements.txt

4. Run the Streamlit App
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streamlit run app.py

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⚙️ Screenshots

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📬 Contact For questions or collaborations, feel free to reach out via LinkedIn or create an issue in the repository.