polerisk

Predictive utility pole failure analysis and maintenance optimization platform

What You Get

polerisk provides comprehensive tools for analyzing utility pole health, predicting failures, and optimizing maintenance schedules. Built for utility companies, infrastructure managers, and risk analysts who need data-driven insights for pole management.

Key Capabilities

🔍 Risk Assessment

• Analyze pole condition and failure probability
• Multi-factor risk scoring (age, material, weather exposure, load)
• Historical failure pattern analysis
• Predictive maintenance scheduling

📊 Data-Driven Insights

• Statistical analysis of pole populations
• Geospatial visualization of risk hotspots
• Time series analysis for degradation patterns
• Cost-benefit analysis for maintenance prioritization

🤖 Machine Learning Models

• Failure prediction models
• Anomaly detection for unusual deterioration
• Risk clustering and pattern recognition
• Custom model training on your data

📈 Reporting & Visualization

• Interactive dashboards
• Exportable reports (PDF, CSV, HTML)
• Map-based visualizations
• Custom analytics for your KPIs

Installation

pip install polerisk

Optional Packages

Install additional capabilities based on your needs:

# For enhanced performance
pip install polerisk[performance]

# For machine learning features
pip install polerisk[ml]

# For web dashboard
pip install polerisk[web]

# For cloud deployment
pip install polerisk[cloud]

# Install everything
pip install polerisk[all]

Quick Start

import polerisk

# Load your pole data
poles = polerisk.load_data('pole_inventory.csv')

# Assess risk for all poles
risk_assessment = polerisk.assess_risk(poles)

# Get high-risk poles
high_risk = risk_assessment[risk_assessment['risk_score'] > 0.7]

# Generate maintenance schedule
schedule = polerisk.optimize_maintenance(
    high_risk,
    budget=100000,
    time_horizon='1year'
)

# Export results
schedule.to_csv('maintenance_plan.csv')
polerisk.generate_report(schedule, output='report.html')

Core Features

Risk Analysis

# Calculate failure probability
risk_scores = polerisk.calculate_risk(
    poles,
    factors=['age', 'material', 'weather_exposure', 'load'],
    weights='auto'  # or specify custom weights
)

# Identify critical infrastructure
critical = polerisk.identify_critical_poles(
    poles,
    criteria=['customer_impact', 'replacement_cost', 'failure_risk']
)

Predictive Modeling

# Train a failure prediction model
model = polerisk.train_model(
    historical_data,
    target='failure_within_year',
    model_type='random_forest'
)

# Predict failures
predictions = model.predict(current_poles)

# Evaluate model performance
metrics = model.evaluate(test_data)
print(f"Accuracy: {metrics['accuracy']:.2%}")

Geospatial Analysis

# Create risk heat map
risk_map = polerisk.create_risk_map(
    poles,
    base_map='openstreetmap',
    cluster_radius=5  # km
)

# Export interactive map
risk_map.save('pole_risk_map.html')

# Find poles in high-risk zones
zones = polerisk.identify_risk_zones(poles, threshold=0.8)

Maintenance Optimization

# Optimize maintenance schedule
optimal_plan = polerisk.optimize_maintenance(
    poles,
    budget=500000,
    constraints={
        'max_poles_per_month': 100,
        'min_risk_threshold': 0.6,
        'region_balance': True
    }
)

# Calculate ROI
roi = polerisk.calculate_roi(
    optimal_plan,
    avoided_failures=estimated_failures,
    failure_cost=avg_failure_cost
)

Data Requirements

polerisk works with standard pole inventory data:

Minimum Required Fields:

• Pole ID
• Location (latitude/longitude or address)
• Installation date or age
• Material type

Recommended Fields for Better Analysis:

• Inspection history
• Maintenance records
• Load data
• Weather exposure
• Soil conditions
• Previous failures

Supported Data Formats:

• CSV, Excel
• JSON, GeoJSON
• Shapefiles
• SQL databases
• REST APIs

Use Cases

Utility Companies

• Reduce unexpected outages by 40-60%
• Optimize maintenance budgets
• Prioritize inspections based on risk
• Comply with regulatory requirements

Infrastructure Managers

• Long-term asset planning
• Capital expenditure optimization
• Risk-based decision making
• Performance benchmarking

Risk Analysts

• Portfolio-level risk assessment
• Scenario analysis and modeling
• Cost-benefit analysis
• Regulatory reporting

Performance

• Analyze 100,000+ poles in seconds
• Real-time risk scoring
• Parallel processing for large datasets
• Cloud-scalable architecture

Support

• 📚 Documentation: polerisk.readthedocs.io
• 🐛 Issues: github.com/kylejones200/polerisk/issues
• 💬 Discussions: github.com/kylejones200/polerisk/discussions
• 📧 Contact: kyletjones@gmail.com

Requirements

• Python 3.12 or higher
• Standard data science libraries (automatically installed)

License

MIT License - Free for commercial and personal use.

Ready to optimize your pole maintenance?

pip install polerisk

Get Started | View Examples | API Reference