SkySpecs Predicts Component Remaining Useful Life

Allen and Joel speak with Allan Larson, VP of CMS Products at
SkySpecs, about their remaining useful life estimates for
operators. By predicting component failures, operators can create
better maintenance schedules, saving time and money. Fill out our
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YouTube channel here. Have a question we can answer on the
show? Email us! Welcome to Uptime Spotlight, shining light on
wind energy's brightest innovators. This is the progress powering
tomorrow. Allen Hall: Welcome to the Uptime Wind Energy podcast.
I'm your host, Allen Hall, along with my co host, Joel Saxum. And
today we're diving into a critical challenge facing wind farm
operators, predicting component failures and optimizing maintenance
schedules. Imagine if wind farm operators could instantly gauge the
cost impact of their decisions And automatically assign a dollar
value to the risk. It sounds like science fiction, but it's
actually becoming a reality through innovative approaches to
remaining useful life assessments and automated risk detection. In
today's episode, we'll explore how these technologies are
revolutionizing wind turbine maintenance. Helping operators reduce
downtime, cut costs, and extend the lifespan of their assets. We'll
learn how advanced analytics and artificial intelligence are
enabling more precise predictions and smarter decision making in a
WinFarm world. Our guest is Allan Larson, the VP of CMS products at
SkySpecs. In his role, Allan leads all aspects of product
development for the Horizon CMS platform, which is crucial for wind
turbine drivetrain monitoring and diagnostics. As part of SkySpecs
product team, Allan manages the product roadmap, conducts market
research, and oversees the development and launch of new features.
His expertise is key. In condition monitoring systems and AI based
fault detection for wind turbines makes him a key player in shaping
innovative solutions for the wind industry. Allan welcome to the
show. Allan Larson: Thank you. Allen Hall: That was pretty good,
wasn't it? That was a pretty good intro. I feel pretty good about
myself now. Play it when you go home from the show here, yeah.
That's the rap, people. Uh, so, you're a drivetrain specialist. CMS
drive space. Allan Larson: Yes. Specialist. These days, that's what
I've become. Yes. Allen Hall: Yeah. And that is, uh, obviously a
really needed, uh, knowledge base, particularly as the number of
wind turbines has grown dramatically and we're rapidly producing
turbines. We also rapidly produce drive train problems. And CMS is
going to be the only way for us to dig ourselves out of a little
bit of a hole on gearboxes and bearings and some of the drive train
issues. Uh, what do you see as sort of the top level issues out in
the field today and what are you, what are you hearing? Allan
Larson: Well, I mean, I think about it not so much in terms of, uh,
which, uh, which failure mode is occurring most today or whatever.
It's more, um, the failure modes that you have today is something
that we need to detect early so we can act on it, right? And, uh,
that's what CMS is all about. It's about this early and accurate
detection of failure. of drive train failure modes so you can take
appropriate action at the appropriate time. Allen Hall: Yeah, it's
been a very busy crane season in the middle of the United States.
We've noticed a lot of gearboxes and main bearings being replaced.
The CMS systems are going to play a bigger part in that. I think a
lot of operators are becoming much more aware that CMS is needed on
drive train. Allan Larson: Yes, um, actually when we, when we
started, uh, the Company Vertical AI that SkySpecs acquired in
2021. When we started that, we thought, uh, our perception of the
market was, say, uh, Europe is in front here, like they're the most
mature, most likely to adopt a new software solution. And then we
thought US is a bit behind based on what we knew about the market.
And we would say, well, I think the US is maybe a decade behind in
CMS adoption. That's it. Uh, and I think it's almost the other way
around now. And so the U. S. market has picked CMS up like crazy.
Really? Yes. So, uh, this is more and more becoming the perception
that you just need to have that. There's no new turbines being
produced in the market that doesn't have a CMS system. Right. The
manufacturer simply can't offer a guarantee without it. Because
they need to make the same maintenance decisions during warranty.
And they need to know about it. Hey. Pending failures, uh, leading
up to an, uh, end of warranty date. And if they want to offer long
term guarantees like FSAs, uh, they need to know what the current,
uh, failure status is in their fleet. And so you do that with
drivetrain condition monitoring. There's some damages you can
detect up to a year, several years in advance. Right. And others
that's months, half a year away. Right. Like I said, we've, we've
sort of. Uh, with our software focused in the beginning, of course,
I'm solving the whole condition monitoring problem. But, uh, now we
turn our attention a lot more towards how to drive action in the
field more efficiently. That's where the remaining useful life
comes, comes into it. Um, How was Allen Hall: that, how was that
implemented? I'm really curious how you think through that as a
problem set and get to an output. What does that look like?
Obviously you're taking out all this data and we know more about
turbines today than we knew 10 years ago. A lot more. There's just
so many more sensors on a turbine than there were, especially
coming out of the factory. Even though I think a lot of operators
do complain that the number of, uh, amount of sensors that are on
there probably isn't enough. However, uh, you got to give the OEMs
credit. There is more data coming down and people are adding their
CMOS systems on top of them. What do you do with that? How do you
process that? What does that look like? How do you attack the
problem of assessment? Allan Larson: As in on the actual condition
monitoring Allen Hall: part? Yeah, how do you look at all that
conditioning monitoring and then helping that site manager make a
decision? Allan Larson: Um, I think the detection problem is too
hard to explain on radio. Laughter Um, and uh, others have done it.
I think I'd rather talk about the, the, um, Well, yeah, that's what
I'm trying to get at is it will kind of surprise you a little bit
on how we approach it because, um, at the moment, it's not so much
about like, Oh, we're going to combine all our data streams and
then produce a magic output. It's actually more of an understanding
of the problem itself. So let's say that you, um, detect something
on a main bearing, detect damage on a main bearing, right? It's,
we're not predicting that something might happen. We detect
something that is happening right now. like a damage that's ongoing
and that will last a certain amount of time. But, uh, and so we,
the diagnostic piece of that is saying, well, it's an, it's an
inner ring fault or it's an outer ring fault, or it's a bearing, a
spalling issue or something like that, right? You can diagnose it
down to a really specific level. Um, but regardless of what it is,
you're going to have to exchange that main bearing at some point.
You can't avoid it. Maybe you can extend the life by greasing the
bearings and purging the grease and re greasing it, so on. But the
sort of prognostic of it, right, the prognosis, sorry, is clear,
right? That main bearing is going to die, you're going to have to
exchange it. Allen Hall: So the remaining useful life is an
interesting concept. Not a concept, I mean, it's an action. But,
obviously, when the designers of a component like a main bearing
come to you and say, Well, The lifetime of this bearing is a
thousand years. Allan Larson: Yes. Allen Hall: And then it's five
and it's toast. Allan Larson: Yeah. Allen Hall: So something's
wrong there. Are you coming in for the remaining useful life and
saying the lifetime of this bearing is actually a lot lower? Which
then increases its cost? Is it based on history? Allan Larson:
Yeah. No, so, um, Allen Hall: Because the predictive failure,
right, the predictive failure rates are built into specs. So the
OEMs are out going to the manufacturers saying, I need to have one
of these out of a million fail. Yeah, well, so, Allan Larson: I
mean, we're talking about a domain where you detect something,
right? You detect, let's say that main variable, the probability of
you having to exchange that main variable is 100%. Sure. It's going
to happen. Sure. It's going to happen. Yeah. But there's a, there's
a, there's a time when there's a step change in the cost and time
on, and the time you have until then, that's basically your
remaining useful life, right? That's what you, what's for you, but
you should be interested in the time until I think I incur a risk.
So instead of saying that you have a probability for a risk, right?
You're talking more about using RUL as a proxy for risk
probability. Okay. Okay, right. So you're Allen Hall: saying
there's a time window where that risk is can occur in or maybe not.
I'll give you the US versus European example. 10 years, repower US.
30 years, Germany probably still running. Allan Larson: Yeah, but
here you're talking more about risk quantification on a fleet
level. So like, should I buy this turbine or not? And like looking
long term projections,