Description:
Led development of an AI/ML-enhanced XR training platform for workforce training solutions that integrated real-time data mapped from physical engine components to digital twins. The platform combined augmented reality-enhanced 3D simulations of equipment maintenance processes with machine learning algorithms that provided pattern analysis for predictive maintenance and anomaly detection.
AI-enhanced Predictive Maintenance & Anomaly Detection
The system's machine learning component performed predictive analysis and anomaly detection, allowing trainees to experience realistic equipment failure scenarios and practice diagnostic procedures before issues occur in real-world operations. Users could toggle between customizable 2D dashboards for quick analytics snapshots and immersive 3D digital twin environments for detailed equipment visualization—providing flexibility for different training contexts and learning preferences. This advanced simulation training reduced instruction time while dramatically improving knowledge retention through hands-on practice in risk-free virtual environments.
Trainees interacted with 3D virtual equipment that represented its physical counterpart, experiencing realistic scenarios including equipment degradation patterns, failure modes, and emergency procedures. The ML algorithms analyzed equipment data in real-time, teaching personnel to recognize early warning signs and perform preventive maintenance—critical skills for operational readiness.
XR Simulations & Work Instructions
Designed for highly regulated environments, the training system met stringent security and compliance requirements while delivering improvements in readiness and operational effectiveness. The platform demonstrated the power of combining immersive learning with AI to prepare personnel for high-stakes situations where traditional training would be costly, dangerous, or logistically complex.
Role: Product Development Lead, Business Analyst & XR Design
Key Features:
XR/AR immersive training environments Real-time data integration from physical equipment to digital twins Machine learning for predictive maintenance, anomaly detection, and equipment failure pattern analysis Risk-free simulation of equipment failures and emergency situations Hands-on practice for complex technical procedures Improvements in training effectiveness and retention