Interscale Content Hub – Digital twins for predictive maintenance are a totally new way of getting insights into how well your assets are performing.
This kind of innovation lets you predict when things are going to fail before they actually do.
What if you could predict when a piece of equipment was going to fail before it actually did? Have you ever thought about reducing costly downtime and extending the lifespan of your assets?
So, let’s take a look at how digital twins are changing the way we do predictive maintenance.
What is the Role of the Digital Twin in Predictive Maintenance?
As we know, the digital twins are always getting data from their physical counterparts via IoT sensors.
As Raymon van Dinter et al., highlight in “Predictive maintenance using digital twins: A systematic literature review,” digital twins provide a real-time representation of the physical machine and generate data, such as asset degradation, which the predictive maintenance algorithm can use.
This means a digital twin is always up to date with the latest data from sensors in the physical asset, so it reflects the asset’s condition and performance.
Integrating this data in real time means we can make more accurate predictions about when equipment will fail and schedule maintenance activities before the actual breakdowns occur.
For example, in manufacturing, digital twins can simulate how machinery operates and predict how long components will last.
This simulation uses data from sensors and controllers built into the machinery, which helps to match the digital model with how the real machine actually behaves.
P. Aivaliotis, K. Georgoulias, and G. Chryssolouris say in their paper “The Use of Digital Twin for Predictive Maintenance in Manufacturing,” how you can avoid the intrusive techniques traditionally used in predictive maintenance by using this approach.
The Intersection of Digital Twins and Predictive Maintenance
The thing about digital twins and predictive maintenance is how they can work together to give you better insights into your assets in real time, which helps you manage them better.
Yingchao You and colleagues in “Advances of Digital Twins for Predictive Maintenance” talk about this as a multi-step process:
- Data Collection: Sensors built into the physical asset collect data on different things like temperature, vibration and pressure.
- Data Integration: The data is then fed into the digital twin, which uses algorithms and machine learning to analyse it.
- Predictive Analytics: The digital twin spots patterns and irregularities, giving you a heads-up on potential issues before they arise.
- Prescriptive Maintenance: The system then suggests the best time and type of maintenance to avoid these predicted failures.
Aivaliotis and colleagues look at how digital twins can change the way we do maintenance by using physics-based models to simulate real-world operations.
These simulations help us predict when things might go wrong and plan maintenance schedules based on what’s actually happening, rather than just looking at historical data.
This approach is backed up by research from Yingchao You and colleagues, which shows how digital twin technology is helping us predict maintenance issues and work more efficiently.
Just to flag up the potential risks, you might want to read “Your Digital Twin’s Evil Twin: A Roadmap to The Risks of Digital Twins.”
Implementing Digital Twins for Predictive Maintenance
We need to think strategically if we’re going to make digital twins work for us. That’s why, Aivaliotis and colleagues have come up with a four-phase process:
- Advanced Physical Modelling: This means building a detailed digital model of the asset, including both its physical structure and how its components move.
- Simulation Tuning: We’re constantly making improvements to the digital model using data from the physical asset in real time. This means the virtual replica always reflects the real-world conditions accurately.
- Digital Twin Operation: Next, we use the tuned digital twin to simulate different scenarios and predict how the asset will behave in various conditions.
- RUL Calculation: We work out how long the parts of the asset will last based on the simulation results and data from the physical asset.
Needless to say, the four steps are quite complicated, but it’s worth putting in the effort.
The insights you get from the digital twin can lead to big cost savings and better asset performance.
One example from manufacturing is using digital twins to keep an eye on and predict how industrial robots are going to wear out.
The digital twin helps you to plan maintenance by simulating how components will perform in real-life conditions and predicting how long they’ll last.
Benefits of Digital Twins in Predictive Maintenance
One of the main benefits is that it makes things more efficient. In Australia, where downtime can be really expensive because of things like remote locations and harsh conditions, this is a huge plus.
If you can predict when things will fail and schedule maintenance based on real-time data, you can make your operations much more efficient.
This approach gets rid of the guesswork and reactive measures that are usually part of traditional maintenance methods.
The financial benefits of digital twins are just as impressive.
For a lot of organisations adopting this technology, cost savings is a big reason why.
By cutting down on unplanned downtime, which can be really expensive, and making assets last longer through timely maintenance, you can make a big impact on your business profits.
As well as saving money, digital twins also help to make workplaces safer.
Predictive maintenance helps us spot and deal with potential problems before they become accidents.
Another plus of digital twins is that they help with decision-making. The real-time data and detailed simulations give managers the insights they need to make informed decisions.
This level of visibility means you can plan and coordinate maintenance more effectively, making sure that it’s done right and on time.
And last but not least, digital twins help assets last longer. If you catch problems early on, digital twins stop minor issues from becoming major failures.
This proactive maintenance strategy means you can keep your assets in good shape and get the best performance out of them for longer.
You might find it helpful to take a look at “Digital Twins in the Built Environment: A Cheat Sheet to Efficient Projects“ for a reference on how to use digital twins in different sectors.
A Support System for Your Digital Twins Process
If you’re finding it tricky to get to grips with digital twins, Interscale can help. How do we do that?
Our approach is to use BIM data and processes to create a solid foundation for digital twins. That way, we can guarantee they’re accurate, work well together, and have smooth workflows.
What does that mean for your business?
That means we’ll be your support system, helping you work more efficiently, make better decisions and bring new ideas to market faster.
Please feel free to drop us a line to arrange a meeting. We’re here for you 24/7 to adjust what we can do with your needs and goals.
Or, you can start with some basic info on our Interscale BIM Management Support page here.
In Closing
The growing use of digital twins is transforming how businesses manage their assets across Australia.
This new technology is helping architecture, engineering, and construction firms to make their operations more efficient, cut costs, and make sure their valuable assets last longer.
That’s why you can rely on the Interscale team to support you at every stage of digital twins for predictive maintenance process.
