How a digital twin facilitates predictive maintenance

Digital twin technology is a virtual representation of a physical asset in a manufacturing facility and this asset, which can be anything from a single control valve to a machine, a production line, or even an entire plant, helps makes predictive maintenance possible.

By Aucotec October 15, 2017

Digital twin technology has the potential to improve plant and product design, optimize energy efficiency, and support data-driven decision making. Perhaps the biggest benefit, which is convincing plant and operations managers to adopt this technology as soon as possible, is the ability to move to a predictive maintenance model.

Approaches to maintenance have changed and are continuing to change, thanks to shifting attitudes, improvements in technology, and, in some cases, regulatory requirements. As a result, more facilities are moving from a reactive model to a preventive or predictive one.

Unlike reactive and preventive maintenance, predictive maintenance (PdM) is a condition-based approach. It relies on data that allows technicians to predict when the equipment will fail. This data is typically collected by sensors that measure a range of variables and send them to a database where the numbers are crunched and compared with normative data to determine when service will be necessary.

In some industries, predictive maintenance can help companies meet new regulatory requirements. For example, in the food industry, the Food Safety Modernization Act (FSMA) requires processors to shift from a reactive approach to food safety to a proactive one.

How does a digital twin facilitate predictive maintenance?

Digital twin technology makes predictive maintenance possible. A digital twin is a virtual representation of a physical asset. This asset can be anything from a single control valve to a machine, a production line, or even an entire plant. The difference between a digital twin and a traditional model or simulation is that the digital twin is responsive—it receives information from sensors on the physical asset and changes as the asset changes.

This facilitates predictive maintenance in a couple of ways:

  • It provides a complete real-time model of the asset and its performance. This allows technicians to look for inconsistencies or abnormal patterns and find problems that may not be easily identified through visual inspection or other traditional methods.
  • As a virtual representation, a digital twin isn’t bound by the constraints of time. That means you can run simulations to predict how the asset will degrade based on factors like age, runtime, or exposure to harsh environments. Using the results of these simulations, technicians can predict how and when the asset is likely to fail, long before it actually does.

For companies, this approach provides many benefits: 

  • Eliminating unplanned downtime: Technicians can solve problems before they cause shutdowns and also schedule maintenance at the time that will be least disruptive to operations, for example, during a shift changeover or other planned downtime.
  • Reducing maintenance costs: Reactive maintenance is expensive in terms of both parts and labor, not to mention the cost of emergency downtime. Preventive maintenance is better, but it often results in maintenance activities being performed before they’re needed. Predictive maintenance is the most cost-effective approach because maintenance can be planned in advance and based on the actual condition of the equipment.
  • Improving equipment performance and reliability: Sensors provide real-time data on asset performance. As we mentioned earlier, those assets can be complete production lines or even entire plants. By monitoring the big picture—not just a single piece of equipment, but the full context in which that equipment operates—technicians can optimize across assets to improve performance and reliability of the whole operation.
  • Extending asset life span: Predictive maintenance reduces unnecessary wear and tear so assets perform their best for as long as possible.
  • Boosting safety: Emergencies often create unsafe situations—for personnel, for equipment, and for the environment. Predictive maintenance boosts safety by drastically reducing the chances that an emergency will occur.

How to implement digital twin technology

The basic requirements are sensors to collect data from the physical asset and a software platform to create the virtual representation. Engineering Base is a unique software for plant and mechanical engineering that provides a centralized database and digital twin functionality across all of your assets.

This article originally appeared on Aucotec’s blog. Aucotec is a CFE Media content partner. Edited by Chris Vavra, production editor, Control Engineering, CFE Media,

Original content can be found at