PS: In your opinion, what are most plants doing right these days when it comes to asset management, even before factoring in AI and machine learning?
MR: Over the past couple of decades, most plants have adopted fully the concept of taking all the data and that big data that they’re gathering and putting it into some sort of historian, for example, like OSIsoft PI, and that allows them to be gathering all this real-world data about their equipment and putting it in there, and starting to gather insights just using straight historian analytical tools. That part has really been accepted as canon within the industry.
Over the last decade or so, you’ve also added on top of this, machine learning based predictive analytics solutions, and also condition-based management solutions. Condition-based is looking for certain thresholds of operation conditions to basically trigger actions out there: “If I’m above a certain pressure flag, send something out and we’ll have to take a look at that.”
Plant operations, they’ve got a really good handle on the base types of maintenance. You know, your standard reactive maintenance. Something fails, we fix it, right, everybody at least has that part. But then going into preventative maintenance, taking those checks and services that have been recommended by the OEM or by good practice for regular checkups on the equipment, condition-based on top of that, we’re looking for those thresholds you mentioned now. All those being internalized, that next level is then predictive maintenance, taking a look at conditions as they’re in their incipient stages.
PS: What percent of plants out there right now would you say are really engaged with this?
MR: My feeling there is that all your larger utilities have some form of this program in place right now and, to a varying degree, your independent power producers are also adopting that, whether they’ve been spun off from a large producer or sometimes they’ll pick it up when they get acquired by asset management that wants to get a good feel on what they own. It’s a combination of self-performed using the tools that are out there, and also using software as a service or monitoring as a service to augment the software. We’ve seen it all around; if I threw a number out there, 70% or so of the market is probably actively using this in some form or another, and the other 30% is down that path, at least to the historian and probably going a little bit further.
PS: My understanding too is that AI and machine learning currently are more strategic technologies, in the sense that they aren’t deployed on every single asset. They’re more deployed on assets that are considered critical assets or at least asset specific basis. Should I update my thinking on that?MR: Surprisingly, it does cover all the assets but you do see them target those big returns on investment. So, your big, prime movers, your gas turbines, your steam turbines, your big pumps, your big heat exchangers, the condenser. Those kinds of things are easy to show that value for, and they’re also ones that have a lot of historical data behind it that we know those modes of failure, and we know the effects of not taking those actions ahead of time, which has really been accepted as canon within the industry.
So, if you’re looking for that quick return on investment, yes, those are the biggest places where you’re going to be able to focus it. But now we’re starting to branch out a little bit further that, and we’re getting to that next layer of insight and knowledge by taking the learnings on top of what we’ve been seeing with straight predictive analytics now and start working into prognostics.
Now I want to know, “All right, what’s happening to me? What are the likely scenarios, and how long do I really have? What’s my event horizon to make a decision before it’s made for me?”
This story originally appeared in the November 2021 issue of Plant Services. Subscribe to Plant Services here.
