Eleven-I: Predicting Wind Blade Failures with Precision

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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www.weatherguardwind.comIntelstor - https://www.intelstor.com 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,