Physics-Informed Prognostics & Predictive Maintenance

TwinElectra's Physics-Informed Predictive Maintenance solutions combine engineering models, operational data, and advanced analytics to predict equipment health, estimate remaining useful life, and prevent costly unplanned failures.

Unlike traditional predictive maintenance systems that rely solely on historical data, our approach integrates first-principles engineering models with real-world operating data collected through telematics, Industrial IoT (IIoT) systems, and onboard sensors. This combination enables more accurate predictions, improved fault detection, and deeper insight into asset behavior under actual operating conditions.

By continuously monitoring equipment health and operating environments, organizations can move from reactive maintenance to proactive asset management, reducing downtime and maximizing equipment availability.

How It Works

TwinElectra's prognostics platform combines:

• Physics-based component models
• Telematics and IIoT data collection
• Real-time condition monitoring
• Sensor fusion and edge analytics
• Machine learning and AI algorithms
• Cloud-based data processing
• Remaining Useful Life (RUL) estimation

This integrated approach enables continuous assessment of component degradation and asset health throughout the equipment lifecycle.

Components and Systems Monitored

Our predictive maintenance solutions support a wide range of industrial and mobile equipment systems, including:

• Traction motors
• Electric machines and generators
• Gearboxes and transmissions
• Axles and driveline systems
• Hydraulic systems
• Pumps and actuators
• Battery systems
• Power electronics and inverters
• Cooling systems
• Industrial machinery and rotating equipment

Key Capabilities

• Remaining Useful Life (RUL) prediction
• Early fault detection and diagnostics
• Condition-based maintenance planning
• Fleet health monitoring
• Asset performance tracking
• Failure mode identification
• Root cause analysis
• Maintenance optimization

By identifying degradation trends before failures occur, maintenance teams can schedule service activities at the optimal time, minimizing disruption to operations.

Advanced Technologies

TwinElectra leverages a combination of modern digital technologies to deliver scalable predictive maintenance solutions:

• Edge computing
• Real-time analytics
• Industrial IoT connectivity
• Cloud computing platforms
• Machine learning
• Digital Twin integration
• Sensor fusion
• Fleet analytics

These technologies work together to transform raw operational data into actionable maintenance intelligence.

Why Physics-Informed Matters

Many predictive maintenance systems rely exclusively on historical failure data. However, equipment often operates under changing conditions that may not be fully represented in past datasets.

TwinElectra's physics-informed approach incorporates an understanding of how components actually wear, age, and degrade under load, temperature, duty cycle, and environmental conditions. This results in more reliable predictions, improved explainability, and greater confidence in maintenance decisions.

Business Benefits

TwinElectra's prognostics solutions help organizations:

• Reduce unplanned downtime
• Increase equipment availability
• Lower maintenance costs
• Extend asset life
• Improve fleet utilization
• Reduce warranty exposure
• Improve operational reliability
• Enhance maintenance planning

By predicting failures before they occur, organizations can maximize uptime, improve productivity, and make more informed decisions about asset management throughout the equipment lifecycle.