Part 1: Digital Twins and Predictive Maintenance

By Allyson Boate / Developer
Nov 04, 2025

As machines and systems grow more connected and complex, the traditional toolbox for managing them feels increasingly outdated. Engineers and operators need new approaches that match the realities of software-driven products and data-intensive environments. Digital twins provide that leap forward. By creating a virtual model of a physical asset and continuously feeding it with real-time data, digital twins reveal both current performance and likely future outcomes. This capability turns predictive maintenance into a practical strategy, giving organizations visibility into asset health and the ability to act before disruptions escalate.

Why digital twins matter now

The rise of edge computing, AI, and advanced analytics has transformed how organizations collect and use data from connected assets. What once sat in isolated systems or batch reports can now be streamed, processed, and acted on in real-time. This evolution makes it possible to build digital twins that do more than mirror performance—they learn from it. By combining real-time sensor data with predictive models and feedback loops, these twins continuously refine their accuracy. The result is a living model that evolves alongside its physical counterpart, turning every data point into actionable insight and every system interaction into an opportunity for improvement.

The Limits of Traditional Maintenance

Traditional maintenance methods include reactive repairs, scheduled servicing, and manual planning. Reactive maintenance waits until equipment fails to take action, which leads to costly downtime and unexpected disruptions. Scheduled maintenance follows a fixed timetable, servicing machines whether they need it or not. This wastes labor, raises costs, and often replaces parts preemptively. Manual planning, once common, adds to the challenge by leaving teams with limited visibility into asset health and resource needs. On top of this, monitoring practices often developed separately across departments, creating silos and inconsistent reporting. Collecting new metrics was usually slow and cumbersome, which prevented organizations from adapting quickly as conditions changed. While these methods were once serviceable, in today’s fast-moving, data-heavy environments they fall short, driving wasted effort, longer outages, higher costs, and dissatisfied customers.

Where the Risks Appear

Consider a manufacturing plant that produces automotive parts with robotic welding machines and conveyor systems. A single faulty sensor on one line brings the entire production process to a halt. Every minute of downtime costs thousands of dollars in lost output, wasted labor, and delayed shipments to carmakers waiting on critical components. Missed deadlines ripple through the supply chain, forcing overtime, rescheduling, and penalty fees. What begins as a small equipment failure quickly escalates into a significant financial and operational setback. In modern manufacturing, downtime is more than an inconvenience, it is a competitive disadvantage.

Predictive maintenance explained

Predictive maintenance monitors, analyzes, and acts on high-frequency operational data in real-time. Instead of waiting for equipment to fail or following rigid service schedules, predictive strategies capture data from sensors and control systems as it occurs, creating a live view of asset health. By processing this data continuously, predictive maintenance provides the speed and scale required to detect early signs of wear, reduce waste, and schedule interventions with precision. Maintenance shifts from being reactive and costly to becoming a proactive driver of reliability and performance.

What is a digital twin?

A digital twin is a virtual representation of a real-world counterpart, such as a car, turbine, or jet engine. These living models stay current with sensor data so that changes in the real world are reflected in the digital version. A twin can be used to monitor and maintain equipment remotely or to forecast how it will perform under different conditions.

By closing the loop between the physical and digital, digital twins give organizations the ability to move faster, learn from real-time data, and apply those lessons across product lifecycles. This feedback loop enables predictive analytics, supports visualization of products in use, helps troubleshoot equipment from a distance, and makes it easier to manage complexity across interconnected systems.

How digital twins work

A digital twin functions as a live model that mirrors the behavior and condition of a physical system. It collects high-frequency data from connected sensors and control systems, then processes that data in real-time to reflect the asset’s current state.

At the edge, IoT devices capture telemetry such as temperature, vibration, torque, and energy use. That data streams through protocols like MQTT or OPC UA into an edge gateway or data pipeline before landing in a time series database. The database maintains a continuous record of every change over time, enabling analysis at the resolution needed for predictive insight.

The Layers of a Digital Twin

Each twin relies on multiple layers that work together to build a complete, evolving model:

• Physical layer: Represents the equipment’s structure and mechanical relationships.
• Operational layer: Tracks live data such as stress, load, throughput, or efficiency.
• Behavioral layer: Applies analytics and machine learning to simulate responses and predict future performance.

These layers synchronize continuously, forming a high-fidelity model that learns as it runs. Predictive and prescriptive analytics transform streaming data into foresight, enabling simulations and “what-if” testing. Models forecast remaining component life, detect subtle performance deviations, and recommend the optimal time to intervene.

In advanced deployments, the twin integrates with automation or control systems to close the loop—feeding insights back to edge devices or supervisory platforms that adjust operations automatically.

This continuous cycle of data, analysis, and feedback helps teams shift from reactive monitoring to adaptive optimization. Through real-time streaming, time series analysis, and predictive modeling, digital twins create a measurable path to reliability, efficiency, and smarter maintenance strategies.

Predictive Maintenance and Digital Twins in Action

An energy utilities group operating a fleet of wind turbines uses predictive maintenance to avoid costly outages. Sensors track vibration, temperature, and wind speed, streaming data into a central platform. Predictive models flag early signs of wear on turbine bearings, allowing technicians to replace components before failure. By pairing this strategy with digital twins, the utility gains a virtual model of each turbine that mirrors performance in real-time. Engineers can run simulations to see how turbines will respond to changing conditions, test different maintenance scenarios, and plan interventions with greater accuracy. This combination reduces unplanned downtime, lowers maintenance costs, and ensures reliable energy delivery to customers.

Next steps

Predictive maintenance represents only the beginning of what digital twins make possible. As digital twin technology matures, the models will support asset optimization, sustainability, and innovation. A twin can reduce emissions by fine-tuning equipment use, extend the life of critical infrastructure, and inform the design of future products. Companies who adopt twins are not only preventing costly failures; They are building the foundation for AI-driven operations and resilient business models. Early adopters are positioned to innovate faster and compete more effectively.

Lead with intelligence: connect assets, analytics, and operations through digital twins that learn and adapt.

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