Digital twin technology has the potential to improve plant and product design, optimize energy efficiency, and support data-driven decision making. But perhaps the biggest benefit — the one that’s convincing plant and operations managers to adopt this technology as soon as possible — is the ability to move to a predictive maintenance model.
What is predictive maintenance?
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.
- Reactive maintenance: Wait until something breaks, then fix it.
- Main drawback: Unplanned downtime that eats into productivity and revenue
- Preventive maintenance: Service or replace assets based on how long they’ve been in use.
- Main drawback: Assets are often serviced or replaced according to a schedule, rather than need
- Predictive maintenance (often abbreviated PdM): Service or replace assets only when necessary, based on data about their condition.
- Main advantages: Eliminates unplanned downtime, reduces maintenance costs, extends asset life, improves efficiency
Unlike reactive and preventive 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.
Eric Martin wrote last year in an article for Food Quality and Safety:
As the FDA shifts its focus from reaction/monitoring to prevention/enforcement, food processors will face a much higher standard for prevention. Production equipment is central to that focus. Reliability, especially the reliability of equipment that is improved through PdM, will be a big part of that challenge.
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.
What do you need 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.
Watch the video below to learn how Engineering Base supports predictive maintenance.