Kevin Clark is the VP of Marketing and Customer Success at Falkonry. A veteran of asset management, experienced as a practitioner and educated as an engineer, Kevin brings over 30 years of experience to the fields of engineering, maintenance, and predictive analytics. As a veteran and advocate in the industrial space, Kevin plays a key role in advancing manufacturing and encouraging new technologies as a thought leader, keynote speaker, and M&R expert. He has served through decades of leadership in the Society of Maintenance & Reliability Professionals (SMRP), International Society of Automation (ISA) and as a long-standing board member of Purdue University’s Polytechnic Industry Advisory Board (IAB). Kevin recently spoke with Plant Services editor in chief Thomas Wilk about artificial intelligence's impact on the worlds of operations, maintenance, and reliability.
PS: Maybe we can start for the few listeners who might not have run into you at conferences, in all your work with Fluke and Falkonry. Tell us a little a little bit about the job you're working in right now, and some of the projects you're working with.
KC: I came on board with Falkonry. I spent a number of years with Fortive with Fluke and Accruent, and I spent most of that time on the strategy side and product management / product marketing, and taking those products to the markets and finding good useful places for them, and making them with practical as we possibly could. So I've had a relationship with Falkonry, and Fortive had invested in Falkonry about eight years ago and I was the point of contact with Falkonry. Through those years we've done panel sessions together, we've done product collaboration together, we've done the “What Ifs?” of AI inside of our products at Fluke or Accruent, and so we've done a lot of things back and forth.
More recently, Nikunj Mehta the founder of Falkonry had come back to me and asked me if I was finally ready to come over to Falkonry and get back into the startup space. And, of course, it sounded intriguing, and the longer we talked, the more it made sense. So I've come over, I've taken over AI deployments, customer support, working on some of the innovations with our customers. I also lead the marketing group and there's a there's a big tie between customer success and marketing and organizations, in how they present themselves to the market and at the same time how they perform with our customers day in and day out.
PS: Thank you for covering both those sides of your current position because I think you're really well positioned to talk about the practical applications of technology that you see in the field while also understanding the wider industry roadmap for these technologies. And you and I caught up but a week ago now at the Reliable Plant show, and figured it was time for us to talk about what AI looks like in industry right now, how companies are applying artificial intelligence. Ever since ChatGPT came along, you can't you can't get away from AI right now in the news and in discussions.
KC: ChatGPT is one of those once in a million kind of opportunities. For us it's a love hate relationship. We love the fact that they gave AI the exposure to the more common population, where they really didn't know much about AI but ChatGPT introduced it to them and got him right into the middle of it right. Now they understand the power of AI, they understand the power of what's been underneath the internet for decades now. And so once they understood that, now they're talking to Falkonry, and trying to understand, well, how does it work? And they're much more knowledgeable today than they were just six months ago. They're asking harder questions or asking more interesting questions.
The problem is, in many cases, their expectations are super inflated, and so bringing that back down to a more practical level, that's been kind of hard to help them understand, what does it mean inside of an asset management world? And what do we actually do with the technology inside of asset management? While it's been great that ChatGPT brought so much exposure to AI, it's also been a bit challenging to calm the waters.
PS: That's an excellent point in that ChatGPT generative AI is good for certain applications. But we're not really talking about that flavor of AI when it comes to what's happening in asset management. And that was my first question for you was, when you think through how you're seeing artificial intelligence applied to asset management right now, process monitoring, what are the 1, 2, 3 things, the challenges or problems that you see AI helping plants solve right now in August 2023?
KC: Some of the things that I see out there, Tom, are things that we've taken for granted over the years. I personally have been in predictive for a long time, and I can claim it; a lot of the people that might hear this would probably say, “yeah, Kevin did fail at that.” But that's been the challenge over the last 20 and 30 years is how do we take RCM and TPM and the really sound methodologies that we utilize inside of asset management, how do we turn that into something digital? We have done a number of things that have made it better in the predictive maintenance world. But we've also done a lot of things that separated us.
One of the biggest challenges we have today is that our operations data is separate from our predictive data. And I see it everywhere I go, everywhere. We've done that, we separated it, because the technologies were somewhat separate, the business units were somewhat separate. But we didn't want to mix it in with the rules and regulations of operational data. (And in fact, operational data really didn't want our asset data, our condition data inside of their MES systems and process monitoring systems.)
The separation made sense because of evolution. But what doesn't make sense is that [asset] data is as important to operations as operations data is to asset data. So what we've been advocating for is that we begin to bring that operations data together with predictive data. We tend to look at data that's continuous, and that's mostly your operations data. Some of that continuous data is your predictive data, it might be coming directly from sensors, or it might be temperature, it might be vibration, it could be could be some ultrasound. But sometimes it's just a moment, right? Like maybe it's a vibration test, but it is time series.
And so you know the time of it, and you know what the result was, and if you take that, and you drop it right into the middle of continuous process data, it’s really interesting. I don't know if you've seen it before. But when you see those signals come together, and you see the performance, and then you see where the failures are in the in the AI data, and then you also see the predictive data coming in that's showing us very similar response to that potential failure – it gets really interesting. If I just have operational data, it's good. If I have operational and predictive data, it's awesome.
PS: Interesting. If I hear you right, when we're looking at is quicker anomaly detection, or quicker anomaly verification.
KC: I would go with detection. Obviously, verification is important, but I would go with anomaly detection, which is what we call it on a regular basis.
Anomaly detection is to me, way more interesting than predictive data. Anomaly detection leads me to predictive faster and more accurately than what I would get from a single test from a vibration sensor. It's like, I only check my heartbeat once in a while, versus I've checked my heartbeat all the time, I'm connected all the time. That's the difference, right? So if I'm able to monitor through AI, which is learning what normal looks like, it's always watching for normal. And so when it sees normal, it gives you a nice color chart, heat map that that makes you feel good, right? When it sees things that are abnormal, it raises the flag, and you see the different colors inside of that heat map, and those colors indicate that something is off.
Now, in fact, it might not lead to a failure, but something's different. We need to understand what that difference is. Ww don't always get that in predictive data because we put a sensor here, we put a sensor over there, we take pictures every now and then, maybe we take some vibration tests, and it's a little bit of luck, right, that we're going to hit just the right time. So I'm a big advocate for getting that predictive data that we've got, plus that operational data that's monitoring always, and let the AI decide if we're starting to move into something that looks different. I like to use the word abnormal, not necessarily unusual, but sometimes it's unusual.
PS: Where would you put this ability to parse that much data on the maturity curve for the technology? I know ChatGPT brought AI into the popular consciousness. Are we looking at technologies here that have been able to do this for the past, say 18 months, or for the past three to four years? Or are we looking at some innovations on the monitoring and anomaly detection side?
KC: Innovations. So anomaly detection has been around for a long time, so has pattern recognition and building models and things of that sort. But anomaly detection has had some innovations that have allowed it to really move quickly. Most of that has more to do with building the right user interfaces, building the right reporting mechanisms, the right notification mechanisms, to really understand what's important.
Now one of the things that's really creative that's coming, is taking anomaly detections and being able to think about them through the idea of a criticality assessment and a FMECA. Most of us that are in the reliability side of the business understand that terminology, and it's kind of the core of what we do inside of a facility that deploys RCM and TPM. It's a very hands-on, in fact it's even got some gut feeling kind of data inside of it. But what we're seeing with anomaly detection, is that we can make an association between what we identify inside of our FMECA, and also understanding the criticality of a particular asset, all the way down to the sub-components, the signals coming in are actually extensions of a FMECA.
We can clearly identify the signals that are associated back to a particular failure mode. It starts to bring to life anomaly detection. It's not only coming back and telling you, “I'm beginning to fail in this particular area of the asset,” it's also going to tell you what the failure could be. Maybe there's three signals that are that are showing a yellow basically. And of those three signals, that usually means something, and we can label that. That's what I really like about the technology that's coming along, is it's starting to look and sound like the reliability that we're used to speaking to.
