Digital Twins

Create virtual replicas of physical industrial systems that mirror real-time behavior for simulation, optimization, and predictive analysis.

What is a Digital Twin?

A digital twin is a virtual representation of a physical asset, process, or system that is continuously updated with real-world data. Unlike a static simulation, a digital twin lives alongside its physical counterpart, reflecting its current state, history, and predicted future behavior.

Digital Twin Maturity Levels

Building a Simple Digital Twin

import numpy as np
from datetime import datetime

class MotorDigitalTwin:
    """Digital twin of an industrial motor."""

    def __init__(self, motor_id, rated_power=100):
        self.motor_id = motor_id
        self.rated_power = rated_power
        self.state = {
            'temperature': 25.0,
            'vibration': 0.0,
            'current': 0.0,
            'rpm': 0,
            'health_score': 100.0
        }
        self.history = []

    def update(self, sensor_data):
        """Sync twin with real-world sensor data."""
        self.state.update(sensor_data)
        self.state['timestamp'] = datetime.now()
        self.state['health_score'] = self._calculate_health()
        self.history.append(dict(self.state))
        return self.state

    def _calculate_health(self):
        """AI-based health score calculation."""
        temp_factor = max(0, 100 - (self.state['temperature'] - 60) * 2)
        vib_factor = max(0, 100 - self.state['vibration'] * 10)
        return (temp_factor + vib_factor) / 2

    def predict_failure(self, hours_ahead=168):
        """Predict if failure will occur within timeframe."""
        if len(self.history) < 100:
            return {'prediction': 'insufficient_data'}

        trend = self._calculate_degradation_trend()
        estimated_rul = self.state['health_score'] / abs(trend)

        return {
            'health_score': self.state['health_score'],
            'degradation_rate': trend,
            'estimated_rul_hours': estimated_rul,
            'failure_likely': estimated_rul < hours_ahead
        }

    def simulate_scenario(self, load_profile, duration_hours):
        """Run what-if simulation without affecting real system."""
        sim_state = dict(self.state)
        results = []
        for hour in range(duration_hours):
            load = load_profile[hour % len(load_profile)]
            sim_state['temperature'] += load * 0.1 - 0.05  # Simplified
            sim_state['vibration'] += load * 0.001
            results.append(dict(sim_state))
        return results

Digital Twin Platforms

Use Cases

• Virtual commissioning: Test production line changes in the digital twin before modifying physical equipment
• Process optimization: Simulate parameter changes to find optimal settings without risking production
• Training: Train operators on a virtual replica of the factory without affecting real production
• Remote monitoring: Monitor factory operations from anywhere with real-time 3D visualization
• Lifecycle management: Track equipment health and plan upgrades based on predicted degradation