PREDICTIVE MAINTENANCE DATE: SEP 2025

Predictive Certainty:
100% Downtime Elimination

The Mission: A Francis/Kaplan fleet struggled with recurring "surprise" failures. Our objective: Install Digital Twins to predict breakdown vectors weeks before they manifest.

MISSION STATUS:
AUTOMATED
Unplanned Downtime
0%

Over 3 Years

Prediction Lead Time
30 DAYS

Advance Warning

Maintenance Budget
-22%

Annual Savings

01 The Threat Vector

The client's fleet suffered from unpredictable outages caused by issues like minor bearing wear and transient electrical faults. Traditional preventative maintenance (calendar-based) failed because these dynamic failures escalated too rapidly between scheduled checks.

> RISK DETECTED: LOW ALARM-TO-FAILURE RATIO
> CONSEQUENCE: REVENUE LOSS FROM EMERGENCY TRIPS

02 Tactical Execution

We deployed the AnoHUB Digital Ecosystem:

  • Digital Twin Synchronization Installed high-fidelity acoustic and vibration sensors to feed continuous, granular data into the virtual replica for real-time comparison against the "perfect" model.
  • AI Anomaly Detection Implemented proprietary ML models trained on normal operational data, allowing the system to predict minute deviations (the true **Anomaly Detection** markers) before they reach critical limits.
  • Condition-Based Shift Maintenance shifted to intervention only when AI confidence exceeded 70%, guaranteeing a lead time of **15 to 30 days** for parts procurement and crew scheduling.
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