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Predictive Risk Analytics

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Gas Sensor Analytics Platform 🚀

A comprehensive dual-dashboard system for intelligent gas detection device monitoring, from individual sensor risk prediction to organization-wide fleet analytics. Built with Plotly Dash and powered by advanced statistical modeling and machine learning.

🎯 Platform Overview

This platform provides two complementary dashboards designed to give you complete visibility into your gas detection IoT fleet—from granular device-level risk assessment to high-level organizational performance analytics.

📊 Two-Level Analytics Approach

  1. Probabilistic Risk Prediction Dashboard - Microscope View 🔬

    • Focus: Individual device analysis
    • Purpose: Real-time risk assessment and failure prediction
    • Users: Maintenance teams, field technicians, safety officers

  2. Organization Risk Metrics Analytics Dashboard - Telescope View 🔭

    • Focus: Multi-organization fleet analytics
    • Purpose: Pattern discovery, benchmarking, and strategic insights
    • Users: Fleet managers, executives, data analysts

🔬 Probabilistic Risk Prediction Dashboard

What It Does

Analyzes individual gas sensor devices using advanced probabilistic modeling to predict failures, detect anomalies, and assess real-time risk levels.

🌟 Core Features

📈 Four Analysis Tabs

  1. Data Visualization

    • Interactive time-series of sensor readings
    • Configurable resampling (5-60 min intervals)
    • Statistical summaries and trend analysis

  2. Bayesian Transitional Probabilities

    • State transition modeling
    • Probabilistic risk assessment
    • Future state predictions

  3. Adaptive ARIMA

    • Time-series forecasting
    • Anomaly detection
    • Confidence intervals

  4. Survival Analysis

    • Device reliability over time
    • Failure probability estimation
    • Kaplan-Meier survival curves

🎛️ Smart Controls

  • Dynamic Windowing: Maintains optimal 100-200 row analysis window
  • Range Slider: Navigate through complete device history
  • Interval Selector: Choose temporal granularity (5-60 minutes)
  • Real-time Updates: Instant recalculation on parameter changes

📍 Device Context

Each analysis includes:

  • Device ID and sensor type
  • Organization affiliation
  • Geographic location (interactive map)
  • Data quality metrics

💡 Best For:

  • Predictive maintenance scheduling
  • Real-time safety monitoring
  • Individual device troubleshooting
  • Failure root cause analysis
  • Compliance documentation

📋 Data Format

Excel file (data.xlsx) with columns:

  • device_id, sensor_type, sensor_reading
  • date_created, organization_id
  • latitude, longitude

🔭 Organization Metrics Dashboard

What It Does

Analyzes multiple organizations simultaneously to discover patterns, benchmark performance, and identify operational clusters using machine learning.

🌟 Core Features

📊 Six Analysis Tabs

  1. Normalized Trends

    • Per-device metric normalization
    • Multi-organization comparison
    • Optional annual markers

  2. Summary Table

    • Interactive filtering and sorting
    • Raw metrics + device counts
    • Export-ready format

  3. Correlation Heatmap

    • Discover metric relationships
    • Identify co-varying patterns

  4. Daily Averages

    • Normalize for month length
    • Compare per-day patterns
    • Identify seasonal effects

  5. 3-Month Moving Average

    • Smooth short-term fluctuations
    • Reveal long-term trends
    • Faceted metric views

  6. K-means Clustering ⭐ Most Powerful

    • Custom metric selection
    • Automatic cluster optimization
    • Silhouette score analysis
    • Cluster centroids heatmap
    • Average trend lines (bold black)
    • Export cluster assignments

📊 Nine Key Metrics Tracked

  1. Consecutive increase sequences
  2. Overlimit threshold exceedances
  3. Fall detection events
  4. No motion alerts
  5. Silent SOS triggers
  6. SOS triggers
  7. Hardware errors
  8. Low threshold detections
  9. High threshold detections

🔧 Normalization Methodology

  • Per Organization Per Device: Metrics divided by device count
  • Min-Max Scaling (0-1): Applied per organization
  • Fair Comparison: Accounts for fleet size differences
  • Relative Changes: Captures organizational context

💡 Best For:

  • Fleet-wide performance monitoring
  • Organization benchmarking
  • Pattern discovery across facilities
  • Resource allocation decisions
  • Anomaly detection (organizational level)
  • Strategic planning

📋 Data Format

Excel file with sheet "Result 1" containing:

  • month, organization_id, unique devices
  • All 9 metric columns

🔄 How The Dashboards Work Together

Workflow Example: Comprehensive Fleet Analysis

1️⃣ START: Organization Metrics Dashboard
   └─ Identify underperforming organization clusters
   └─ Spot unusual patterns in specific facilities
   └─ Note high hardware error rates in Org #47

2️⃣ DRILL DOWN: Probabilistic Risk Dashboard
   └─ Load data for devices in Org #47
   └─ Analyze individual sensors with high error rates
   └─ Use Bayesian analysis to predict failure timing
   └─ Check survival analysis for replacement priority

3️⃣ ACTION: Informed Decision Making
   └─ Schedule preventive maintenance
   └─ Order replacement parts
   └─ Monitor improvements in next cycle

Use Case Scenarios

🏢 Scenario 1: Fleet Manager

  • Morning: Check Organization Dashboard for overnight alerts
  • Mid-day: Identify cluster of organizations with rising SOS triggers
  • Afternoon: Use Risk Dashboard to analyze specific devices
  • Result: Proactive maintenance prevents critical failures

🔧 Scenario 2: Maintenance Team

  • Issue: Device showing erratic readings
  • Step 1: Risk Dashboard → Load device history
  • Step 2: ARIMA tab → Detect anomaly patterns
  • Step 3: Survival Analysis → Estimate remaining useful life
  • Result: Data-driven replacement decision

📈 Scenario 3: Executive Leadership

  • Question: Which organizations need intervention?
  • Step 1: Organization Dashboard → K-means clustering
  • Step 2: Identify low-performing clusters
  • Step 3: Risk Dashboard → Validate with device-level data
  • Result: Strategic resource allocation

🎓 Key Capabilities Comparison

Feature Probabilistic Risk Organization Metrics
Focus Single device Multiple organizations
Time Granularity Minutes (5-60) Monthly aggregates
Analysis Type Predictive modeling Pattern discovery
Primary Users Technicians, safety officers Managers, executives
Data Volume 100-200 rows/device Months/years of aggregates
ML Techniques ARIMA, Bayesian, survival K-means clustering
Output Risk scores, forecasts Clusters, benchmarks
Update Frequency Real-time capable Periodic (monthly)
Visualization Time-series, maps Trends, heatmaps, clusters

🚀 Getting Started

Probabilistic Risk Prediction Dashboard

  1. Prepare Data: Create data.xlsx with device sensor readings
  2. Run Application: python app.py
  3. Access Dashboard: Navigate to http://localhost:8050
  4. Select Device Window: Use range slider for time period
  5. Choose Interval: Pick resampling rate (30 min recommended)
  6. Explore Tabs: Start with Data Visualization, then move to analysis tabs

Organization Metrics Dashboard

  1. Prepare Data: Create Excel file with "Result 1" sheet
  2. Launch Dashboard: Access via deployment URL
  3. Upload File: Use file upload interface
  4. Select Organizations: Choose facilities to compare
  5. Explore Normalized Trends: Understand baseline patterns
  6. Run Clustering: Discover organizational groups automatically

📦 Technical Stack

Shared Technologies

  • Plotly Dash: Interactive web framework
  • Plotly Express/Graph Objects: Visualization
  • Pandas: Data manipulation
  • NumPy: Numerical computing

Risk Dashboard Specific

  • Statistical Models: Bayesian inference, ARIMA, survival analysis
  • OpenStreetMap: Geographic visualization

Metrics Dashboard Specific

  • scikit-learn: K-means clustering, silhouette analysis
  • Dash Bootstrap Components: Modern UI components

🎯 Strategic Value

Operational Benefits

  • ✅ Reduce Downtime: Predict failures before they occur
  • ✅ Optimize Costs: Data-driven maintenance scheduling
  • ✅ Improve Safety: Early warning system for gas detection
  • ✅ Enhance Compliance: Comprehensive audit trails

Analytical Benefits

  • 📊 Multi-Scale Insights: Device to organization level
  • 🔍 Pattern Recognition: Discover hidden operational trends
  • 📈 Benchmarking: Compare performance across facilities
  • 🎲 Risk Quantification: Probabilistic rather than binary alerts

Strategic Benefits

  • 🏆 Competitive Advantage: Superior fleet intelligence
  • 💰 ROI Optimization: Better resource allocation
  • 📱 Scalability: Handles small to enterprise deployments
  • 🔮 Future-Ready: Extensible for new metrics and models

🤝 Support & Contribution

Getting Help

  • Review individual dashboard READMEs for detailed documentation
  • Check data format requirements carefully
  • Ensure all required columns are present

Contributing

Both dashboards are designed for extensibility:

  • Additional statistical models
  • New clustering algorithms
  • Custom visualization types
  • Enhanced export capabilities
  • Real-time data streaming

📄 License & Acknowledgments

Built with ❤️ using Plotly Dash

Powered by:

  • Statistical Modeling 📊
  • Machine Learning 🤖
  • Probabilistic Inference 🎲

🎉 Ready to Transform Your Gas Detection Fleet Management?

Start with the Organization Metrics Dashboard to understand your fleet's big picture, then dive into the Probabilistic Risk Prediction Dashboard for device-level intelligence. Together, they provide unparalleled visibility into your gas detection operations.

From Reactive to Predictive. From Data to Decisions. From Sensors to Strategy. 🚀

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