Show notes
This episode Allen and Joel speak with Bill Slatter, CEO of Eleven-I, about their innovative blade monitoring technology. Eleven-I's sensors provide real-time data to detect and prevent blade damage, potentially reducing maintenance costs and improving turbine efficiency. Gain insights into the challenges of wind blade lifetimes, the importance of proactive monitoring, and the future of blade condition monitoring systems in the wind energy industry.
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Allen Hall: Welcome to the Uptime Wind Energy Podcast. I'm your host, Allen Hall, joined by my co host, Joel Saxum. As we have all experienced, wind turbine blade failures can lead to costly downtime and repairs. And Eleven-I is tackling this challenge head on with their innovative monitoring technology. Their systems provide real time data that helps increase efficiency and reduce maintenance costs.
And if you are new to Eleven-I, they are based in the UK. Near Manchester, England. Today, we're joined by Bill Slatter, CEO of Eleven-I. We'll be discussing the challenges in Windblade Lifetimes, Eleven-I's solutions, and the impact on the industry. Bill, welcome to the show.
Bill Slatter: Thanks for having me.
Allen Hall: There has been a number of horror stories over the last several months in regards to Blades And I know Eleven-I has been called into action on some of those because I've dealt with the operators on those projects but there does seem to be a lot of blade issues at the moment.
And it mostly, at least in my opinion it evolves from not knowing what is actually happening with the blade.
Bill Slatter: So one of the things that Eleven-I is trying to do is not just detect damage, but help understand what's causing most of those damaging conditions. It's something that we've. We've been trying to pioneer is yeah, picking out what causes damage, not just picking out when it's happened.
Is that already too late? I think that's one of the things that the industry is picking up on. We need to Obviously pick out that damage earlier on. What would happen if we could actually get to the point where we're preempting damage and stopping it happening?
Joel Saxum: So I think Bill, that's one of the things of course we've known each other for a couple of years now, and that was one of the things that originally, when I was in my blade life attracted me to you and your solution.
Of course, I like working with you because you're a nice guy. But, on the other side of that, it is what Eleven-I brings to the table as far as its CMS technology, and you immediately caught me when we had our first call and you showed me a presentation about, and you're like, this is an active movement of what's happening in the blade now, And you guys are doing things rather than, hey, we've detected a crack, it's, we have these physics engines, we're trying to do, we can, we're looking at modeling fatigue over lifetime, we're trying to understand why these issues are happening, or being able to warn operators or give them flags of hey, you're overloaded here, or you've got this going on, Before, and what we feel like a lot of other CMS systems do, they're like, Hey, problem, flag, come and inspect.
So can you walk us through a little bit about what sets the Eleven-I solution apart from the rest of that Blade CMS marketplace?
Bill Slatter: Absolutely. I think one of the sort of things that perhaps differentiates us from some of our competitors is that we're active in a number of similar markets.
So we've got systems that are being used for in blade test facilities to help understand the bit that blade behavior when they're in the testing phase. We've also been used by OEMs to help understand the behavior of wind turbine blades. They're the newest, it's a prototype turbine. And they want to know how that may differ from models and actually see how that blade behaves in the real world.
So the type of work that we do in these engineering projects really help. help us to understand what real life looks like and what blade behavior should be, or maybe it shouldn't be. And that helps us then get to the point where we can help people understand what is causing that damage. And also, I've said that before, but We have detected damage when it's occurred, we've also been dropped onto blades that they know have damage or very high susceptibilities to damage and successfully detected those damage modes.
I think that's the big thing is that, if we set our mission statement, it would be detect damage, detect the causes of damage, and then try and prevent damage.
Allen Hall: Yeah, it does seem particularly with newer blades, We don't have a lot of service history. We don't really know what those failure modes are.
And because as we've seen on a number of operators, the blade sets are made in different factories in different parts of the world that, which may have different materials built inside them and different approaches to building those blades. The mechanical response of. A set of blades on a particular turbine may not be the same response as the turbine next to it.
That is a huge problem area at the moment for the wind industry. What do we do about that? How, what is, what, first of all, what do you think is driving some of that besides manufacturing? Is it just because we don't understand some of the physics involved? Are we guessing we're getting newer, Modes of failure because of the blade length?
Bill Slatter: New technologies enormous blades, reductions of safety factors and then as shorter innovation time as possible this is why we're in the position where we are. Nobody wants this to happen. But part of the way out of it is to use systems like ours to help understand what's actually happening on your blades.
The blades are generally fairly neglected in terms of condition monitoring. Some of the bigger blades may have some sort of load sensing systems in there. But it's not something that has been done as commonplace yet. But obviously the industry knows that requirement is coming.
We want to be part of it.
Joel Saxum: I had a customer that had a problem they knew was a highly susceptible to an issue. And when we scoured the market for what can we use to, to detect this, that what is that next level of CMS that can really dive down into frequencies and all these different kinds of things and have the engineering prowess behind it in the 11i team to be able to tell us what's actually going on here.
We used you guys, and that installation was basically on that project. Now, of course, I'm sure they're all different, but on that project it was three sensors in each blade. All amalgamated to one control box with power and comms to it. And then you guys were able to, of course, through your dashboard and everything, be able to see what was going on, map, look at trend lines over time, put some great reports together and help that client.
That was a specific case, right? We knew what we were looking for and we needed a piece of kit to do it. And I think what makes it something that shines to me here is that Alan and I have regular conversations with say like R and D test systems testing big blades and doing fantastic things in that realm.
But there is, there's just some reality to, Putting sensors in advanced sensors and understanding what's happening out in the real world, because you can only test so much, even if it's hybrid testing, throw in some AI, some machine learning, the biggest freaking 25 megawatt generator test beds and all these things.
You can only test so much in a lab, but you really need to be able to dive in to get real data in the field. That's something I think that sets you guys apart, the ability to collect that high frequency, real good data to be able to do the engineering projects from. And what I want to ask you is, and of course, in respect to any NDAs that you have in place, is there anything that you can share with us of a brief case study of something you guys have done or a problem you've solved for someone in the field.
Bill Slatter: There's a number, obviously a number of case studies. The project that we worked together is helping the customer understand the best behavior that they wanted to eliminate. Through that project, we also picked up on some of the anomalous behavior that we detected. So whenever we get involved in any of these projects, we try and.
We, there's often a problem statement from the customer, but we always will deploy all of our analytical methods to that data and highlight that to the customer. So I won't go too deep into what was found there. But, we didn't just go we went outside the scope of what we set out to do.
It's probably worth talking a little bit about some of the work that we were done with OEMs. Because using the same equipment that we use for that we use in the field for problem solving when people know we have an issue like the type of project that you and I did together, Joel we use the same kit, and we may have a greater number of sensors, and we may be able to get further out down the blade, but we, this is essentially the same kit that's used For these really in depth validation projects that's used for these smaller projects.
And obviously every time you do a project there's learnings from that. So the more systems you get out there,