BIMScaler Blog – One of the best things about digital twin technology is how it helps us predict when things might need maintenance. But how do digital twins enhance predictive maintenance?
The digital twin’s ability to mirror the real-world asset and capture its every heartbeat through a constant stream of sensor data is what makes it so special.
This virtual copy is a goldmine of information, showing patterns and irregularities that might otherwise go unnoticed.
The digital twin doesn’t just tell you when something’s wrong; it also predicts when something might go wrong.
That’s why we’re going to look at how digital twins and predictive maintenance are connected.
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ToggleDigital Twin Technologies in Predictive Maintenance
The marriage of digital twins and predictive maintenance is powered by a whole bunch of cool tech.
The Internet of Things (IoT) is at the heart of it all. It’s a network of sensors and devices connected to collect and share data in real time from physical assets.
This data feeds into the digital twin, creating a live, up-to-date picture of how well the asset is doing.
Next, AI and machine learning algorithms take over. They analyse the data to spot patterns, flag up any irregularities, and predict when something might go wrong.
The insights from this analysis help businesses make smart decisions about maintenance.
The goal is to ensure the right interventions are made at the right time, and a cost-effective price.
The literature review by Raymon van Dinter and colleagues in “Predictive maintenance using digital twins- A systematic literature review,” shows how often machine learning approaches like Support Vector Machines and deep learning techniques like autoencoders and convolutional neural networks are used in this area.
But remember, the choice of technology isn’t one-size-fits-all.
The “IDC’s Cross-Industry Digital Twin Framework: Where Innovation Meets Strategy,“ shows how it’s important to adapt digital twin strategies to suit different industries.
This is because the way data is sourced, integrated, and regulated varies from one industry to another.
When it comes to specific sectors, Australia’s AEC industry, which relies a lot on machinery and infrastructure, is set to benefit a great deal.
Using digital twins, AEC companies can spot when equipment is going to break and avoid downtime, which means they can get more done.
Learn more: “How Can Digital Twins Improve Operational Efficiency? Here’s the Data.”
What is the Role of Digital Twins in Predictive Maintenance?
There are two main ways that digital twins can help with predictive maintenance.
They can spot when equipment is going to fail, and they can also give you useful insights that you can use to make improvements across the whole system.
As van Dinter et al. said, digital twins use real-time sensor data from physical systems to build predictive models.
This means we can work out exactly when maintenance is needed, using the equipment’s historical data and current conditions.
The van Dinter study also points out some of the challenges in this field, like the computational burden and the complexity of the models.
These can be solved with the right platform integration and AI algorithms.
For Australian businesses, using digital twins for predictive maintenance isn’t just about making things run more smoothly.
IDC reckons that by 2027, 35% of big companies will be using digital twins in their supply chains, which should make them 15% more responsive.
These new developments will help local industries stay competitive on the global stage.
They’ll make maintenance more than just a reactive process — it’ll be a proactive driver of innovation and efficiency.
Now, how do digital twins enhance predictive maintenance?
How do Digital Twins Enhance Predictive Maintenance? A Practical
The idea behind digital twins in maintenance is to create a virtual copy of a physical asset or system – it’s a dynamic, constantly evolving replica.
This isn’t just a 3D model; it’s a living, breathing thing that reflects how the real-world asset behaves, performs, and even degrades over time.
The digital twin does this by constantly getting data from sensors in the physical asset.
This data could be anything from temperature and vibration readings to operational parameters and environmental conditions.
The review by van Dinter and co-author shows there are lots of different data sources used in digital twin models.
The most common ones are vibration, velocity, torque and temperature.
Once we’ve got all the data, we feed it into the digital twin, where the advanced analytics and machine learning algorithms do their thing.
These algorithms look at the data and spot patterns, flag up anything out of the ordinary, and predict when something might go wrong.
The digital twin can also help you make better decisions about maintenance.
It can tell you when and where to intervene, and make sure you’re not spending more than you need to.
The IDC’s report shows how important it is to link the digital twin up with existing systems like Enterprise Resource Planning (ERP) or Computerized Maintenance Management System (CMMS).
Learn more: “What are the Key Benefits of Using Digital Twins in Manufacturing?“
Benefits of Digital Twins in Predictive Maintenance
The best thing is you can switch from reactive to proactive maintenance.
Instead of waiting for equipment to break and then rushing to fix it, you can plan for potential issues and deal with them before they cause problems.
This means less downtime, more productivity, and better operational efficiency.
Plus, digital twins can also help cut costs by making maintenance schedules more efficient, reducing the need for spare parts, and lowering energy use.
Also, digital twins help businesses make their assets last longer.
If we know how equipment degrades, we can tailor maintenance to suit each asset, keeping it at its best for as long as possible.
This not only saves money but also helps the environment by reducing waste and using fewer resources.
The research by van Dinter et al., shows a lot of studies are looking at how long components will last, which is useful for planning when to replace them and avoiding unexpected failures.
And last but not least, digital twins can help make things safer and reduce risks.
By testing equipment in a virtual environment, we can identify and address potential issues before they cause accidents or injuries in the real world.
How to Optimise Digital Twins and It’s Predictive Maintenance Feature
The key to successful predictive maintenance is making sure your digital twin models are always up to date and in line with what’s going on in the real world.
Our team at BIM Scaler provides model auditing and maintenance services, preventing Revit file corruption, which could derail your project’s progress.
We do weekly model health checks and regular updates to make sure that every digital twin is an accurate reflection of your project’s real-time status.
This way, there’s no room for guesswork and data silos can’t form. That way, everyone’s in the loop and projects run smoothly.
The idea is that you not only make your models more accurate and easier to use but also get the most out of predictive maintenance.
Ready to see how this works in practice? For all the details, kindly read our BIM Management Support page.
Or, even better, let’s grab lunch. We’ll have a discussion, have a laugh, and figure out how to make those complex digital dreams a reality, one step at a time.
In Closing
The great thing about digital twins is how companies can predict when things are going to go wrong and act before they actually do.
This tech not only makes critical machinery last longer, but it also makes things safer and more efficient across the board.
So, how do digital twins enhance predictive maintenance? The answer is complex. But it’s going to get a lot more complicated if you don’t start it now.