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Let’s start at the beginning

I always thought it would have been cool to have a twin. Someone to share things with, someone who might be just like me but, well, different.
It wasn’t to be, but my younger sister does a pretty good job all the same. You don’t get to pick and choose with humans, but what about inanimate objects? Objects like wind turbines, well heads, or bridges, for instance. Can they have twins? Having a counterpart of something that you can’t even see or get to very easily would be quite something, wouldn’t it? Imagine being able to make smart, informed decisions about their operation and determine whether a critical part is about to fail. Sound too good to be true? Read on to find out more.

Becoming less physical and more digital

Even with little imagination, I guess most people realize that wind farms and oil & gas wellheads can be in remote places. You’re unlikely to find one in the center of a city or just outside your office. But they need maintenance, right? If they’re thousands of miles away and you can’t even see them, how do you know? In the past, fixed maintenance schedules worked because they erred on the side of caution based on extremely conservative safety margins.

Maintaining your car or the boiler in an office block is one thing, but sending highly specialized maintenance engineers to the far corners of the globe or to the depths of an ocean can be a very costly, not to mention dangerous business. But what’s the alternative?

The alternative is called a digital inspection. A digital inspection means that you inspect the digital twin of an asset. You see all operating details and the stresses and strains to which the real asset has been subjected – and still is being subjected – in nothing less than real time. And you can do this from anywhere. Sound too good to be true?

The magic

Assets are digitized using engineering simulation algorithms (e.g. finite element analysis), Newton’s laws of motion, and classical theory of elasticity. The aim is to produce a virtual state of the asset that is as close as possible to the real, physical state. Once we’ve digitized the asset, we need some kind of input from the real world to trigger corresponding movement on the digital twin. This input typically comes from real sensors that are fitted to physical assets. The following image shows just how close to the real thing digital twins can be:

To see this model in motion, look at the video: Digital Twin Model.

At the top of the physical model, which could represent a wind turbine or other industrial asset, is a single physical accelerometer. From this single sensor feed, we’re able to gain a wealth of insights into the state of the asset – in real-time. So, imagine the asset is thousands of miles away, and there you are, sitting at your desk, and able to monitor all different aspects of the asset. But it’s not just the movement per se that is important. Now this is where the finite element analysis, Newton’s laws of motion, and classical theory of elasticity really come into play. The colors on the model change as the asset moves back and forth and is subjected to changing levels of stress and strain.

These insights – and don’t forget we’re talking real time here – enable you to make informed decisions about when, how, and even if you need to change aspects of your asset to optimize operations and to limit structural fatigue.

The bottom few lines

So, we know that objects like wind turbines, well heads, or bridges can have a twin. A twin that comes incredibly close to showing the actual state of the real asset. These digital twins can transform asset operations by removing the rigid, reactive elements from the asset operation and maintenance equation. You can extend the lifetime and efficiency of assets using real-time, predictive engineering analyses. And you can gain insights into asset fatigue and analyze the remaining lifetime of assets.

So, where does SAP fit into the equation? SAP’s latest offering for managing assets, SAP Predictive Engineering Insights, can give you these vital insights. For more information, check out SAP Predictive Engineering Insights on SAP Help Portal.

This blog just scratches the surface of digital twins for industrial assets. Stay tuned for even more information in subsequent blogs.

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  1. Giorgio Barbetta

    Hi Ian,

    I like it, my compliments.

    I’m studying hard the world of Digital Twins so I think that there could be a tipping point for the banking in a world dominated by IOT paradigm but I’m not a clear idea yet : )

    A quick question: (coming from others environments) I’d like to have your different opinion from mine on what could be the role of the banking in an IOT world?

    Is it possible making a digital twin of a discrete banking process?

    Thanks in advance for your kind collaboration


  2. Ian Armstrong Post author


    Hi Giorgio,

    I’m glad you like it!
    There are many, many situations in which digital twins can be used. If I’m honest, I’ve only really thought about – and had exposure to – digital twins of physical objects up to now. But with the ongoing digital transformation in pretty much all spheres of business and technology, there might well be a place for the less obvious candidates, too.
    I’m not familiar with in-depth banking processes, so unfortunately I can’t really comment on those.
    Maybe others out there have some interesting insights?

    All the best,


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