VoltryPredict: See Failures Before They Happen
91% prediction accuracy. 24-48 hour early warning. Powered by federated AI learning from 50M training hours.
How It Works: Federated Learning
Learn from everyone's data without anyone sharing their data. VoltryPredict trains on patterns across hundreds of facilities while keeping your data 100% private.
Local Training
AI model trains on your facility data—never leaves your infrastructure.
Encrypted Updates
Only model weights are shared, encrypted end-to-end. No raw data transmitted.
Global Aggregation
Central server combines learnings from 50M training hours into master model.
Enhanced Predictions
Improved model deployed back to your facility. Everyone benefits, privacy intact.
Real Predictions, Real Savings
Actual case studies from VoltryPredict deployments. These aren't hypotheticals—these failures were predicted and prevented.
Transformer Failure Prediction
Anomaly Detected
VoltryPredict flags unusual thermal patterns and harmonic distortion in transformer T-3.
Failure Probability: 89%
Model predicts critical failure within 48-72 hours based on 500+ similar historical cases.
Emergency Maintenance Scheduled
Team dispatched, replacement transformer ordered from nearby facility.
Controlled Shutdown & Replacement
Load transferred to backup systems. Transformer replaced during scheduled maintenance window.
Critical Failure Avoided
Post-mortem confirms winding insulation failure would have occurred within predicted timeframe.
Cooling System Cascade
Compressor Degradation
ML model detects subtle vibration signature changes in Chiller 2 compressor.
Cascade Risk Identified
System predicts failure would overload Chiller 3, causing chain reaction.
Preventive Intervention
Compressor bearings replaced, refrigerant levels optimized.
Multi-Unit Failure Prevented
Analysis confirms cascade failure prevented, avoiding 72+ hour production halt.
Arc Flash Prevention
Insulation Breakdown Warning
Partial discharge sensors + AI detect degradation in 15kV breaker contacts.
Arc Flash Risk: High
Model predicts 82% probability of arc flash event during next switching operation.
Emergency De-Energization
Circuit isolated, visual inspection confirms carbon tracking on insulators.
Component Replacement
Breaker contacts and insulators replaced under controlled conditions.
Catastrophic Event Prevented
Arc flash with potential for injury and $1M+ in equipment damage avoided.
Validation: The Numbers Don't Lie
91% accuracy validated across 10,000+ predictions in production environments.
True Positive Rate
93%Failures correctly predicted before occurrence
False Positive Rate
8%Unnecessary alerts (still worth investigating)
Average Warning Time
36 hoursTime between prediction and actual failure
Equipment Coverage
47 typesTransformers, switchgear, UPS, generators, HVAC, etc.
Validation Methodology
Retrospective Analysis
5+ years of historical failure data from 200+ facilities used for model validation
Live A/B Testing
Real-time predictions compared against actual outcomes in production environments
Third-Party Audit
Independent verification by IEEE Power Engineering Society working group
Your Data Never Leaves Your Facility
Differential privacy and federated learning ensure zero-knowledge training. We get smarter without ever seeing your data.
What We DON'T See
- Raw sensor readings or telemetry data
- Equipment serial numbers or asset identifiers
- Facility locations or network topology
- Operational schedules or load profiles
- Maintenance records or failure histories
- Vendor-specific configurations or settings
What We DO Share
- Encrypted model gradients (mathematical patterns only)
- Anonymized feature importance rankings
- Statistical aggregates across 50M training hours
- Differential privacy noise (ε=0.1, δ=10⁻⁵)
- Model performance metrics (accuracy, latency)
- Threat intelligence (attack patterns, anomalies)
Differential Privacy Explained
Mathematical guarantee that your individual data cannot be reverse-engineered from the global model, even if an attacker has access to all other facilities' data.
ε = 0.1Privacy budget (stricter than Apple's)
δ = 10⁻⁵Failure probability (1 in 100,000)
k = 5Minimum anonymity set size
Interactive ROI Calculator
Calculate your potential savings based on your facility size, equipment value, and downtime costs.
Want the Technical Details?
Download our whitepaper: "Federated Learning for Predictive Maintenance in Critical Infrastructure"
Whitepaper Contents:
- Model architecture (temporal convolutional networks + LSTMs)
- Training methodology (federated averaging with secure aggregation)
- Privacy guarantees (differential privacy proofs)
- Validation results (10,000+ production predictions)
- Integration guide (API documentation, SDK examples)
- Case studies (detailed analysis of 50+ deployments)
42-page technical paper • Peer-reviewed • Published in IEEE Transactions
Ready to Stop Reacting and Start Predicting?
Join those leveraging 50M training hours with VoltryPredict to prevent failures, reduce downtime, and save millions.
14-day proof of concept • Deploy on-premise or cloud • No data leaves your facility