Digital Twins in Industry: What they are and how they drive predictive maintenance 

Digital transformation in industry is advancing through technologies that bring the physical and virtual worlds closer together. In this context, digital twins have emerged as one of the most promising innovations, enabling industrial assets to be represented virtually in a dynamic and real-time manner. 

More than just static simulations, digital twins use continuous operational data collected by sensors to create virtual models that accurately reflect the behavior of machines and systems. This opens the door to more assertive decision-making in predictive maintenance, asset management, and process optimization, with tangible gains in MTBF, MTTR, and ROI. 

In this article, you’ll learn what digital twins are, how they work in practice, and why this technology is transforming the way industries operate. We’ll also explore how Dynamox ecosystem enables this approach through smart sensors, connectivity, and advanced industrial data analytics. 

What are digital twins? 

Digital twins are virtual models of physical assets that use real-world data to simulate, monitor, and predict the behavior of those assets over time. These are dynamic digital models, continuously updated with field data, that faithfully reproduce the performance and operating conditions of industrial machines, equipment, or systems. 

The concept originated in the early 2000s, linked to the aerospace sector, but gained momentum with the rise of the Internet of Things (IoT), which enabled continuous data collection through smart sensors. In industry, this evolution allowed digital models to move beyond theoretical simulations and begin reflecting, in real time, what is happening with physical assets. 

Today, digital twins are key components of industrial digital transformation, integrating with strategies for predictive maintenance, process optimization, and asset lifecycle management. With the support of sensors and analytics platforms, it becomes possible to predict failures, analyze operational scenarios, and make data-driven decisions—even before physically intervening in the asset.

What’s the difference between simulation, real-time monitoring, and digital twins?

Although simulation, real-time monitoring, and digital twins are interconnected concepts, each has a distinct scope, purpose, and level of complexity in the industrial context:

Simulation

Refers to mathematical or computational models that replicate the behavior of an asset or system based on predefined parameters. It is used to test hypotheses, validate designs, or analyze hypothetical scenarios, typically without direct connection to the actual asset. While useful for planning, simulation is limited in its ability to reflect real operating conditions.

Real-time monitoring

Involves the continuous collection of operational data directly from assets via sensors. This approach enables tracking of variables such as vibration, temperature, and electrical current, allowing anomaly detection and alert generation. However, it provides a “snapshot” of the asset’s current state without necessarily modeling its behavior over time.

Digital Twins

Digital twins integrate both concepts. They combine virtual modeling with real-time monitoring to create a dynamic and intelligent digital replica of the physical asset. This replica evolves as the asset operates, continuously fed by real data, enabling predictive analysis and more assertive interventions.

How do digital twins work in practice? 

Digital twins act as a bridge between the physical and digital worlds, transforming operational data into actionable intelligence. In summary, the operation of a digital twin can be described through a flow that integrates sensors, connectivity, and analytical intelligence: 

Here’s how this technology is structured and what elements make it viable in the industrial context: 

Sensors as the foundation for digital modeling

The creation of a digital twin begins with the installation of IoT sensors on industrial assets. In the case of Dynamox, DynaLoggers are responsible for capturing variables such as vibration, temperature, and electrical current with high frequency and resolution. These data reflect the asset’s actual behavior and are essential for the digital replica to accurately represent its operating conditions.

Secure transmission and connectivity

The data collected by the sensors are automatically transmitted via industrial gateways, such as DynaGateway. Secure industrial connectivity ensures that information flows continuously to the cloud, even in structurally complex environments.

Cloud storage and processing

Once in the cloud, the data are securely stored and processed, enabling continuous analysis and a complete historical record of each asset’s condition. This decentralized architecture allows remote monitoring of equipment installed across different units or regions.

Integration with analytical platforms

Platforms like Dynamox Platform bring all this data together in an integrated environment, where users can visualize asset health, receive intelligent alerts, and perform predictive diagnostics. Integrated into this structure, DynaNeo acts as the digital twin itself. It is within DynaNeo that operational, historical, and predictive data are transformed into a complete and dynamic visualization of asset condition.

A dynamic and evolving model

By aggregating asset data, historical analysis, and predictive intelligence, DynaNeo behaves as a dynamic digital model that continuously evolves as new information is received from the plant. It reflects the current condition of assets, enables failure prediction, supports intervention planning, and anticipates operational deviations.

Watch the video below to see how this tool can support your industry:

What are the benefits of digital twins for industry? 

The adoption of digital twins marks a significant leap in operational reliability, especially in plants already operating with sensor-based predictive maintenance. By combining virtual modeling with real-time data, this technology enables a new way to understand, plan, and optimize the performance of industrial assets. Here are the key benefits: 

Asset health visualization 

With digital twins, technicians and engineers gain immediate access to the behavior of monitored assets. Integrated dashboards provide a consolidated and up-to-date view of each equipment’s status, enabling fast and informed decision-making — even in distributed industrial environments.

Predictive diagnostics and failure reduction

Continuous analysis of real asset behavior allows early identification of failure patterns. In this way, the digital twin acts as an “intelligent mirror,” detecting subtle deviations before they become critical failures. Additionally, AI tools like DynaDetect can be integrated to deliver predictive diagnostics based on historical data and trend analysis — all centralized within DynaNeo dashboards. 

Technical planning before interventions 

By analyzing asset behavior under different operating conditions, digital twins allow companies to evaluate the impact of an intervention before it’s physically executed. This reduces uncertainty, improves resource allocation, and increases the efficiency of maintenance windows. 

Performance optimization and extended asset lifespan 

With detailed tracking of performance over time, it becomes possible to fine-tune operational parameters, improve lubrication regimes, avoid overloads, and ultimately extend asset lifespan. This data-driven approach helps reduce operational costs and prevents premature replacements. 

Measurable gains: MTBF, MTTR, and ROI 

The use of digital twins directly impacts strategic maintenance indicators: 

• MTBF (Mean Time Between Failures): tends to increase due to better failure prediction and prevention. 
• MTTR (Mean Time to Repair): is reduced thanks to more accurate diagnostics and well-planned interventions. 
• ROI (Return on Investment): is enhanced by avoiding unplanned downtime, improving asset efficiency, and optimizing resource utilization. 

In short, digital twins transform operational data into maintenance intelligence, promoting greater reliability, predictability, and control over plant assets. 

Practical applications of digital twins 

The use of digital twins in industry goes far beyond virtual asset visualization. This technology enables monitoring, forecasting, planning, and optimization across various stages of operation — especially in areas such as predictive maintenance, industrial asset management, and process efficiency. Here’s how digital twins are applied in practice: 

In predictive maintenance 

Predictive maintenance is one of the areas most positively impacted by digital twins. Through continuous monitoring based on real asset behavior, failures can be anticipated with greater precision using data collected by smart sensors. This makes condition monitoring more reliable and responsive. 

Moreover, digital twins allow visualization of failure impacts across the entire plant. When combined with historical operational data, they enable more robust predictive analyses and better-planned technical interventions. 

In asset management 

By integrating multiple data sources throughout the asset lifecycle, digital twins offer a complete view of industrial equipment performance. This traceability facilitates tracking of wear, performance, and maintenance history — strengthening strategic decision-making. 

Additionally, with this digital model, technical interventions can be planned more accurately by analyzing the effects of different approaches beforehand to choose the most efficient in terms of time, cost, and operational impact. 

In process optimization 

Digital twins enable advanced analysis of production scenarios, considering operational variables and real asset behavior. This makes it possible to identify optimal process adjustments and enhance plant performance. Immediate adjustments to operating parameters also become feasible, driving gains in productivity, energy efficiency, and equipment durability.

Challenges in adopting digital twins 

Despite their significant benefits, implementing digital twins in industry still presents technical, operational, and cultural challenges that must be addressed. Understanding these obstacles is essential to ensure successful adoption and effective return on investment:

Initial investment and integration complexity 

Building a digital twin involves acquiring sensors, gateways, connectivity infrastructure, and analytics platforms, as well as integrating with existing systems when necessary. This process requires substantial upfront investment — especially in plants with a large number of assets or distributed systems. 

Quality and reliability of input data 

A digital twin is only as reliable as the data feeding it. If sensors are inaccurate, poorly positioned, or if communication fails, the virtual model may reflect a distorted asset condition. Ensuring reliable data collection with smart sensors — such as those from Dynamox — is a critical step.

Interoperability with legacy systems 

Many industrial plants still operate with legacy systems not designed for integration with modern platforms. This lack of interoperability hinders data exchange, limits the digital twin’s reach, and may require technical adaptations or specific APIs to ensure secure connectivity between environments. 

Technical training for teams 

Adopting digital twins requires a mindset shift among maintenance, reliability, and automation teams. Engineers and technicians must be trained to interpret digital models and make data-driven decisions. Cross-functional collaboration between IT, maintenance, and operations becomes essential for successful implementation. 

Information security and data protection 

Since digital twins operate with continuous data — often stored in the cloud — information security is a top priority. Operational data must be protected against unauthorized access, leaks, or tampering. This includes end-to-end encryption, access control, and the use of certified platforms like those from Dynamox, which comply with ISO 27001, ISO 27701, ISO 27018, and ISO 27017 standards. 

Frequently asked questions about digital twins in industry – FAQ 

Can every asset have a digital twin? 

In theory, yes. However, in practice, digital twins are more viable for assets whose operation directly impacts plant productivity, safety, or cost — such as rotating equipment, continuous systems, and critical assets. Simpler or low-impact assets may not justify the investment.

What data is needed to build a digital twin? 

To build a reliable digital twin, continuous operational data is essential — such as vibration, temperature, electrical current, operating time, and load cycles. The higher the frequency and quality of this data, the more accurate the model will be. Additionally, historical maintenance and failure records contribute to more precise analyses. 

Do digital twins work offline? 

While some data may be accessed locally, the true value of digital twins lies in continuous updates and real-time analysis, which typically require connectivity. Therefore, the tool performs best when online, reflecting instant changes in asset behavior. 

How do I know if my plant is compatible with this solution? 

Most industrial plants can adopt digital twins, provided there is feasibility for sensor installation and connectivity infrastructure. It’s important to consider asset criticality, required data volume, integration with existing systems, and the digital maturity of the operation. Consulting a technical specialist — such as Dynamox team — is the best way to accurately assess compatibility. 

How Dynamox applies digital twin principles 

Dynamox applies digital twin principles through a complete ecosystem that connects smart sensors, secure connectivity infrastructure, and advanced analytics platforms. 

It all starts with continuous data collection via DynaLogger wireless sensors, which monitor variables such as vibration, temperature, thermography, current, and voltage — and even integrate lubrication data directly into the assets. These data are then automatically and securely transmitted via DynaGateway, including through mesh networks that ensure connectivity even in complex industrial environments. 

Once collected, the data is processed on Dynamox Platform, a cloud-based system that organizes, interprets, and applies analytical intelligence to generate predictive diagnostics and operational insights. 

Furthermore, the specialized AI engine DynaDetect identifies failure patterns and behavioral deviations, while the integrated dashboards of DynaNeo provide a clear and intuitive view of asset health — enabling technical planning based on real data. 

This technological structure effectively creates a digital twin for each monitored asset, with dynamic modeling and continuous evolution as new data is collected. As a result, maintenance and reliability teams gain a powerful tool to predict failures, analyze scenarios, optimize interventions, and boost operational efficiency. 

Do you want to understand how digital twins can be applied in your industrial plant? Talk to a Dynamox specialist.