Python-based analysis and categorization system for 21,800 cable network segments (6,300 km) using time series analysis to identify high-priority sections for preventive maintenance.
This project analyzes medium voltage cable network performance using historical failure and anomaly data to prioritize maintenance activities. The analysis processes multiple data sources to categorize 21,800 segments and identify 284 high-priority segments requiring immediate attention.
Business Impact:
- 📈 Analyzed 21,800 cable segments covering 6,300 km of network
- 🎯 Identified 284 high-priority segments for preventive maintenance
- 🚀 First initiative of this type in the organization
- ⚡ Enables proactive maintenance planning and resource allocation
- 💰 Reduces network failures and improves service reliability
- Python 3.x
- Pandas - Data manipulation and analysis
- NumPy - Numerical operations
- OpenPyXL - Excel file processing and formatting
- PyArrow - Parquet file handling
cable-network-analysis/
├── README.md
├── requirements.txt
├── notebooks/
│ └── cable_analysis.ipynb # Main analysis notebook
├── data/
│ ├── input/ # Sample input files
│ └── output/ # Analysis results
└── images/
└── (optional screenshots)- Historical failure and anomaly data
- Network inventory (cables, transformers, installations)
- Geographic and operational data
- Planning and project information
- Time series analysis of failure patterns
- Anomaly detection and trend identification
- Risk scoring based on multiple factors
- Geographic and operational context integration
- Segments ranked by criticality
- Resource allocation recommendations
- Preventive maintenance planning
- Output formatted for operational use
- ✅ Processes multiple data sources (CSV, Excel, Parquet)
- ✅ Time series analysis for failure pattern detection
- ✅ Multi-factor risk scoring algorithm
- ✅ Automated categorization of 21,800+ segments
- ✅ Identifies high-priority maintenance targets
- ✅ Professional Excel output with formatting and tables
- ✅ Scalable to large network datasets
# Install required libraries
pip install -r requirements.txt
Execution
Place input files in data/input/:
4-Etapas.csv
Base Anomalias-Averias MT.xlsx
Dicc_INV.xlsx
Elementos_PYO.xlsx
Inventario_MT.parquet
Open and run notebooks/cable_analysis.ipynb
Output will be generated in data/output/Plan26.xlsx📊 Results Network Coverage:
21,800 cable segments analyzed 6,300 km of network coverage Comprehensive risk assessment Actionable Insights:
284 high-priority segments identified Clear maintenance prioritization Resource allocation guidance Output Format:
Professional Excel report Formatted tables and data Ready for operational planning 🎯 Use Cases This analysis supports:
Preventive Maintenance Planning: Identify cables before they fail Resource Allocation: Prioritize maintenance crews and budgets Risk Management: Focus on highest-impact segments Strategic Planning: Long-term network improvement initiatives Performance Tracking: Monitor network health trends 📝 Notes Sample data provided represents the structure but not real business data Original implementation analyzes complete network inventory Time series analysis uses historical failure and anomaly patterns First initiative of this type implemented in the organization 👤 Author Federico Cantillana - Data Analyst & Team Lead 📧 Email: fede.canti@gmail.com 🔗 LinkedIn: linkedin.com/in/fede-canti 💼 Portfolio: [Your Notion Portfolio]