Hi everyone. Everybody can hear me well from the back.
Okay. Good.
So, to introduce,
I work for ZF Windpower is not any company doing software.
What we are doing are turbines.
Okay. We produce pieces that go to produce wind power actually.
But I'm part of the digital team.
Why do we have digitalization?
We'll talk about later on.
Okay. Like that.
But to start, let's tell a bit about my story with wind.
I didn't start just working with turbines.
My love for wind dates back much earlier when I used to live in the beautiful Marseille.
Marseille, nice town for who has been.
Less than six hours from Brussels and it has a great resource, wind.
At the time, I used to do sailing.
You come to the Vierpart, city center of Marseille,
have your pasties and you will see beautiful boats.
You go out, the sea is nice.
What has this to do with the energy transition?
Well, historically, Marseille, the main industry was fossil fuels, oil.
Okay. We have a place called Fossilmer.
Great place to sail.
It has great wind and one of the best winds in Europe.
Just in front now of the oil factories, what do we have?
The latest technology of turbines, floating turbines, means devices that you can put on the sea.
Very deep, very recent technology.
Those are, if not the most modern, among the most modern of France.
The power is 8.4 megabatts and now they are three.
They can already power a small city like Martig.
They're just close to it for who is familiar with the area.
So from the love for wind, from sailing, now I can see that this gets to the energy transition.
Right?
Is it only Marseille?
No. What happens in Marseille does not stay in Marseille.
Valid also for energy.
These are graphs that I created myself from data from Kaggle.
So open source data.
What do you see there?
Well, you can see that around the world we have some big production of renewables in general.
And I guess there will be somebody here who has been or is from South America.
South America is a place where you have strong input from hydropower.
But at least to stay here in Europe, wind here is a big deal.
And it's increasing.
If we look at countries like Denmark, we are already almost at half of the energy production.
National energy production of wind.
It's a combination of good wind, because up north is really good, and of politics of wheeling it.
I told about Marseille, there is good wind, but France is not even close to the measure.
That means wind is definitely one energy resource that we will use more and more.
Very important.
And produces at a big scale.
We have already now in Marseille 25 megawatts.
It's huge, only with three turbines.
All good? All great?
Well, we have some problems in general with big installations.
Things can go very wrong.
Very wrong.
And this is not just a matter of changing a small component.
Let's say that a turbine, like even in land, not as big as the one on Marseille, gets faulty.
It has to be stopped.
What happens?
Notification processing.
It has to go, and we need to tell someone.
Two days, then there will be an inspection time.
I get the team to inspect the fault.
Two weeks.
And if I have locally the component, the replacement,
will be six weeks to replace it, but maybe much longer.
Component may be on the other side of the world, even.
We don't know.
And then the repairs, a couple more weeks.
So for a turbine, which is 3.5 megawatts, not the latest technology like I show in Marseille,
the whole intervention, if you are lucky, let's say 10 weeks, is lots of money, 125k, at least.
A lot.
How do we tackle this problem?
By forecasting and by optimization of spare parts, so getting the spare parts already
in-house, and to get it start as quick as possible, faster return to operation.
And this is done by treating data.
What do we do?
Ideally, we monitor and predict.
So there is an alert.
It has to go to the cloud.
I have to classify the failure already in the cloud.
I had to know what is about.
I have to prescribe a solution.
I have to find the spare parts.
And I have to forecast when I need to apply my solution.
What, where, when.
That means alert, data are collected and analyzed, and compared to historical data.
The graph that you see there is like a exponential curve.
I don't get into the Bible modeling.
Here is not the moment of mathematics, but is a model that predicts well when cumulative
failures will occur, subjected to a certain type of failure.
So that's the type.
I don't, as I said, don't get to technical.
This is not the moment, but we can discuss it later on.
How do we do that?
Well, wind turbine data come, production data come.
So this is from us, from people who produce.
We get it to the cloud and we do prescriptive maintenance.
What means prescriptive?
Well, let's see quickly.
Reactive.
I fix the failure.
Okay.
I have a puncture on my bike.
I change.
Preventive.
I do it regularly, like changing the oil in the car.
That's what is typically done also for bigger installation.
Predictive.
That's what I talked about just now.
Prescript is AI.
Okay.
Familiar with that.
So AI tells you this is going to happen.
Please do this.
Data analysis with open source software allows more and more sophisticated maintenance.
We have just been talking so far about AI, power by Python and so on.
We have seen very good demonstrations earlier on.
And what is our digitalization tech stack?
So to get more specific, I already introduced Python.
Then pandas to treat data frames, to treat data, at least to a small scale.
Lifelines is a package that implements ViBool and is open source.
Myself, I had already my own version where I added some modules.
Docker, Git, of course, we work as a team.
And it goes to Azure DevOps.
Notice, I told about the cloud.
I told about DevOps.
DevOps is not just a technology, it's a way of working.
That's very important.
The technology allows to work us in an agile manner.
And that's how we get results.
So what we want as a result, reduced on time.
We want to shorten.
Instead of six weeks, let's get less or 10 weeks.
Reduce the cost.
So we need to have a proper stock level.
And for that, we need predictions.
Reduce template maintenance.
We don't want the scenario where we have to call the technician.
And the technician has to come from the other side of the world.
And to avoid, of course, the consequential damage by addressing recurring failures.
Okay, if I see that a certain bearing is always failing, let's address that.
So,
why?
How do I know that I'm not just talking hot air?
Okay.
This all sounds great.
Okay, you use AI, you use open source.
Does it work?
Well, what did we do?
Then as producers, as manufacturers, pardon.
We went to one of the customers and we proposed a pilot project.
We apply our techniques.
Let's see.
The results.
Okay.
How did it go?
Well, 50% less of alert processing.
All these alerts.
So, unplanned field inspections, 60% less.
We strongly reduced the lead time to repair because we could forecast and we had the right
parts in stock.
And in general, the annual energy production got up to 0.5% of all the park with also with
all the turbines.
This means lots of money.
Of course, for corporate reasons, I cannot tell too much, but you can figure out.
To conclude, okay?
Without, then we can go more technical if you like, guys.
But take away messages are fragmented value chain affects very badly the wind energy efficiency.
Don't have value chain, which is all dispersed.
Okay.
In general.
But data insights and very good communication from the data.
Has great benefits.
We reduced the alert processing effort.
We have prescriptive maintenance, which allows us to decrease the time to repair and we increase
the overall efficiency.
So, the annual energy production is one of the main KPIs for the public.
So, we have a lot of data.
So, the annual energy production is one of the main KPIs for wind power, but in general for any energy source.
All these could be achieved with open source software.
Okay.
I sort of saw the full stack.
Finally, it was the devos practice, not only the software that allows us the success of the pilot
project.
And I guess here we have a lot of people who are familiar with devos.
Now, I guess I still have a couple of minutes for questions.
Anyone?
See.
Hi there.
I used to work for Siemens wind power and they had a predictive maintenance team.
I'm just wondering, have you found any other companies as you've put open source kind of using your tools?
Well, I'm not dealing directly with the customers.
In general, with the customers, we just propose our solutions and we exchange the data.
For example, if Siemens Gamesa has failures, we communicate the failures to the dev and we can
suggest a stock amount for a certain component, for a certain turbines.
But it's not that we are like the software company going to sell that.
You see, it's more like the normal customer relationship when you sell parts.
But how can we make, let's say, predictions?
How we can interact?
How we can serve better the customers even as a company?
Good analysis of data and for that we use open source.