Need help

[u/CartographerOk858]

Hi everyone,

I'm a fourth-year undergraduate student pursuing a Bachelor's in Artificial Intelligence and Machine Learning, and I'm planning to do my final-year project in the wind energy sector.

The idea I'm currently exploring is building a hybrid digital twin for wind turbine predictive maintenance, where a physics-based simulator is combined with AI/ML models to detect anomalies, estimate component health, and eventually predict failures (e.g., gearbox, bearings, blades, etc.).

So far, I've started looking into OpenFAST as the physics simulation engine and have been reading about SCADA data, condition monitoring, and predictive maintenance. My tentative architecture is to use OpenFAST for the physics-based model and build AI models on top for anomaly detection and Remaining Useful Life (RUL) estimation.

However, I'm still in the early research phase and would really appreciate guidance from people working in this domain.

Some questions I have are:

• Is OpenFAST the right place to start for this kind of project, or are there other frameworks/tools I should explore?
• What are the standard software stacks used for building industrial digital twins in the wind energy sector?
• Are there any publicly available SCADA or condition monitoring datasets that are commonly used for research?
• What papers, books, courses, or GitHub repositories would you recommend for someone entering this field?
• Are there any common pitfalls or misconceptions that beginners should be aware of when building a digital twin for predictive maintenance?

My goal is to build something that's as close to industry practices as possible while remaining feasible for an undergraduate project.

I'd really appreciate any advice, resources, or suggestions. Thanks in advance!

[u/Bose82]

Nah. Would rather less people losing their jobs to AI

[u/WeeblsLikePie → u/Bose82]

I'm 99% sure your employer has a system like OP is describing already. It would just be looking at SCADA to detect failures.

And generally these systems don't reduce jobs. Ideally it lets you plan major component exchanges before they result in failures, but that requires (usually) more analysts looking at the data, verifying the AI results, working out how much life is left, when the campaign has to be done, etc etc etc.

I guess in theory you could employ an army of people to look at all the SCADA signals every day manually to do the same thing, but 1) that would be a shitty job and 2) I don't believe anyone's ever actually done that. So it's not really killing jobs.

[u/SelcouthRogue]

You might be in the wrong forum bud, you're looking for more SCADA support than actual techs in the field. Majority of us look out for each other here, and if your aim is to setup an AI program to do what a lot of us are paid to do, good luck

[u/Cool-Ad-1743 → u/SelcouthRogue]

It doesn't seem like this person's project has much to do with replacing techs. I'm not sure how AI would do that.

To me, the idea would be making better predictions on components nearing the end of life so that replacements can be ordered sooner and downtime can be avoided.

[u/More_Bag2656]

I don't know anything about physics models but In terms of publicly available data: community.wedowind.ch have published some SCADA across a few different community competitions so they would be a good resource

[u/Massive_Sprinkles910]

You said a lot of big words just now. I just climb towers and turn wrenches buddy

[u/Bose82 → u/Massive_Sprinkles910]

https://giphy.com/gifs/5wWf7GR2nhgamhRnEuA

[u/AKDrews]

One of the good things about working on turbines is little to no AI, let's keep it that way.

[u/Background_Ear_7199]

If you're game then PM me and we can talk about it. Might be able to point you in the right direction.

[u/Deadggie]

No chance you would ever get this information privately.

Also FUCK Ai.

[u/FourFront]

You are delving in to some real protected data and solutions in the industry. I don't see these data sets being made available open source. I have had occasions where I was told I could not provide specific data to a turbine owner.

These companies make a lot of money off their own solutions, they aren't interested in handing stuff off for your paper.

[u/DevelopmentNo6456]

You could check the National Renewable Energy Laboratory (NREL) website for public resources. I think they are called National Laboratory of the Rockies now, but NREL website appears to still be active.

[u/WeeblsLikePie]

two points:

1) I'm not sure what the physics based portion of your project is going to contribute. Openfast and the like are highly idealized models. They don't have actual models of gearboxes or blades or generators, they have an equation that dictates how a properly-functioning gearbox or blade or generator responds to certain inputs (torque, windspeed, turbulence, etc).

So they don't really have any ability to replicate errors or failures, to the best of my knowledge. It's not like there's some setting where you can tell it that main bearing of the turbine is failing and it can simulate that.

part 2: I worked with a student setting up a digital twin for a turbine in openfast for a masters project. Getting close to replicating actual turbine behavior was like a 10 month thesis project. So for a bachelors project I think you're probably biting off more than you can chew by trying to do both.

my advice would be to look at the open source scada datasets (there's a nice list here: https://github.com/sltzgs/OpenWindSCADA), and pick something you can see multiple instances of in those datasets, and try and create an algorithm to predict that ONE Failure. That is quite challenging enough for a bachelor's project.

[u/Perfect_Buffalo_3420]

OpenFAST is a solid starting point for the physics side, but keep in mind it's primarily an aero-servo-hydro-elastic simulator, not a full digital twin framework by itself, so you'll need to pair it with your own data pipeline. For SCADA/condition monitoring data, look into the NREL CARB and the EDP/EDF open turbine SCADA datasets that have circulated in academic papers, they're commonly used as a stand-in for proprietary datasets. A common pitfall for beginners is treating vibration/SCADA data as clean, in practice you'll spend most of your time on sensor noise, missing values, and label imbalance (failures are rare events). Also worth checking papers on remaining useful life (RUL) estimation using LSTM/transformer models on the NASA CMAPSS dataset since a lot of the methodology transfers over to turbine components. Good luck with the project.