NDE 4.0 Podcast | Transcript | John Lindberg, EPRI | Episode 15

NDE 4.0 Podcast Transcript

Episode 15 — NDE 4.0 Progress in the Energy sector

Our Guest: John Lindberg, EPRI

Editor’s Note: In the interest of time, this transcript is still in rough format and has not been edited for proper grammar and punctuation. If you have a need for a fully edited transcript, please contact us.

John Lindberg EPRI – NDE40 Episode

[00:00:00] John Lindberg: Welcome to floodlight Software’s, NDE 4.0 podcast,

[00:00:17] Nasrin Azari: where we interview various experts in industry 4.0 concepts, issues, and technologies for non-destructive testing and inspections. This show is the place to

[00:00:26] John Lindberg: go to learn about the biggest challenges and

[00:00:28] Nasrin Azari: opportunities around NDE 4.0 from some of the smartest people in the industry.

[00:00:34] John Lindberg: So sit back and be prepared for a really thought-provoking

[00:00:37] Nasrin Azari: discussion. Hope you enjoy the

[00:00:39] John Lindberg: episode.

[00:00:54] Nasrin Azari: Hello everyone, and welcome to today’s episode of floodlights N D E 4.0 podcast, [00:01:00] where we pose five questions to a variety of N D E 4.0 experts and explore the benefits and challenges in this emerging field. Today we are joined by John Lindberg, who is very recently retired from the Electric Power Research Institute, epri, where he was responsible for research and development of new and innovative N D E technologies and collaborative development of technologies that may have applications across the electric power industry.

[00:01:26] John received his Bachelor of Science in Environmental Science and Resource Man Research Resource Management from Lehigh University and is a licensed professional engineer in chemical engineering, registered in the state of Pennsylvania. Prior to epri, John worked in various engineering and N D E leadership positions for Pennsylvania Power and Light.

[00:01:46] General Electric, nuclear, nuclear, and Ava. John has over 40 years of experience in the nuclear industry, primarily in the areas of managing non-destructive examination in-service inspection, [00:02:00] refueling, maintenance, and engineering activities. I met John at a conference earlier this year where he presented on some of his real world experiences helping organizations adopt NDE 4.0, and I’m really excited to hear more about those experiences and tease out John’s learnings in our interview today.

[00:02:17] Welcome John to floodlights, N D e 4.0 podcast.

[00:02:21] John Lindberg: Okay. Thank you for having me. I, um, appreciate the opportunity to, uh, have this discussion on ND 4.0. Awesome.

[00:02:30] Nasrin Azari: So as you know, I’ve got five questions for you, John, that are designed to dig into some of the most meaningful and interesting aspects of NDE 4.0.

[00:02:41] I’d like to start with a topic that has come up a few times and some of my earlier conversations, and I’d love to hear your thoughts on it. Based on your experiences to date, do you think N D E 4.0 is about technology or about industry culture change or something else?

[00:02:58] John Lindberg: Well, you know, as [00:03:00] I’ve, I’ve begun to work on ND 4.0 at, at first I thought it was all about technology, but as I’ve gotten more involved in it, and, and certainly more recently, I believe it’s really more about culture change.

[00:03:14] I think the technology, a lot of the technology that we’re talking about implementing in N D E 4.0 is existing technology, but it’s really how we, you know, start adapting that technology and integrating that technology so that we’re better communicating with one another, um, in the new digital environment, um, that we’re going to be in.

[00:03:40] So, In general, you know, what I’ve seen through the years through my career is that whenever you make a change, and especially, um, I’ve worked in the nuclear industry, they’ve been very risk adverse. Um, to new technologies, to adapting new technologies. And, [00:04:00] uh, it’s, it’s been very difficult to, um, implement new technologies unless they’re proven elsewhere.

[00:04:08] So, you know, one of the things that, that we’ve had to do through the years is prove that a technology works. Once you prove that technology works, then you know, typically, um, it’s, it’s adopted. More readily, but still with ND 4.0. ND 4.0 is is much. It’s, it’s much more of a concept than it is specifically dealing with technology or automation or robotics.

[00:04:34] It’s really looking at, you know, how we’re going to change, uh, make, make more of a change to our whole ecosystem. So it’s not just about the technology, but it’s the whole ecosystem and the whole organization. So to make organizational changes. Uh, it, it’s, it’s definitely, it’s definitely a culture change.

[00:04:56] So it’s, you know, the things we have to focus on when [00:05:00] we start adopting, uh, ND 4.0 is, you know, how we change that culture, um, with the organizations that we’re dealing with. And, and so, you know, that’s, that’s a pretty widespread. You know, that’s pretty widespread because it can really depend on, you know, who is the end user, um, who is developing, who’s developing the technology that’s going to be used in ND 4.0.

[00:05:27] And then ultimately, you know, how you integrate that with, with the, you know, all the various end users. So I would definitely say it’s a culture change.

[00:05:37] Nasrin Azari: Yeah. You know, and that’s sort of resonant with a lot of what I’ve heard from other folks too, that, you know, the technology is really there. It’s, it’s really, there’s resistance to adoption for a variety of reasons.

[00:05:51] And people in general are, you know, change is difficult for people and changing the way they do [00:06:00] business, which. Is the case with ND four ND 4.0 is is often difficult. I thought it was interesting that you mentioned in terms of the culture change that um, ND 4.0 affects communica how we communicate with one another, which I thought was really interesting too, cuz we often think about communicating with different technologies, but you also, um, pointed out that.

[00:06:27] The way we communicate with each with one another is actually gonna change as well.

[00:06:33] John Lindberg: Yes, and, and that’s, that’s a good point. I think, you know, one of the things through, um, through my experience that, that I’ve seen is that when, uh, N d e goes out and performs an inspection, whether it’s via visual inspection or a volumetric inspection.

[00:06:49] You get results of that inspection and, and typically, especially if the results indicate that you have a, a flaw or an indication [00:07:00] of concern, then you’ve gotta communicate that up, up the chain, you know, through your direct, the, the parties you’re directly dealing with. Uh, at the utility or, or with the vendor or whate, whatever it, whatever it may be.

[00:07:16] But typically, you know, when you’re, you start talking up the chain, people know less and less about N D E, right? Uh, they, they, they may more, they may know more about engineering and engineering management. Or operations and operations management at the plant. And as you go up, you know, especially at going up to sea level, executive level at the plant, they don’t understand the information that you’re providing them.

[00:07:44] You know, when you provide an inspection report that says, Hey, you know, we, we just found a flaw that that needs to be repaired. And, and so consequently, you know, what we’re trying to do here in ND 4.0 is to, to really establish. And it, and it’s, and it’s [00:08:00] through technology, but it’s, it’s, it’s gonna take a while to get there.

[00:08:03] But, but through technology established means that the data can be explained to somebody, um, that is not knowledgeable and n d e and explain it in a manner that they can understand. You know, what, what the importance of what that means and be able to make, you know, quicker, better decisions, um, with the information that they’re provided.

[00:08:29] Yeah. So, you know, a lot of time, you know, what I’ve seen through the years is a lot of time has been wasted in terms of trying to communicate the meaning of the data. And you know, that’s, that’s one of the things I think nd 4.0 is gonna bring about is, is a means to, you know, provide, uh, integrate that data into other databases or other data systems.

[00:08:53] Um, basically, you know, things like virtual reality are going to have you help you display that [00:09:00] data so that anyone that needs to use that data, Can understand that data very easily and, and can certainly, you know, um, be able to make decisions from that data.

[00:09:11] Nasrin Azari: Yeah, that’s really interesting. That’s

[00:09:14] John Lindberg: an important

[00:09:14] Nasrin Azari: part.

[00:09:16] Yeah. Well, speaking of communication, um, when you’re working with your customers, how do you sell n d E 4.0 when they seem to be stuck in industry 3.0 or in a comfort zone that limits their potential?

[00:09:34] John Lindberg: Well, I, I think, I think the way we have to sell it is sell it through, um, case studies that have been successful.

[00:09:42] Mm-hmm. And, and so it’s not gonna, we’re not gonna be able to show, oh, nd 4.0 has done this across the industry. Uh, it, it’s basically showing specific case studies where we’ve adopted an ND 4.0 technology. In particular, one that [00:10:00] I, I can think of that uh, we worked on recently was, um, using artificial intelligence to perform what we call assisted analysis of, uh, automated ultrasonic examination data.

[00:10:15] And, and typically when you, um, Automate, uh, any examination, uh, but specifically say visual examinations or ultrasonic examinations for every hour worth of data that you’ve collected? Uh, through an automated process, it typically takes, uh, the analyst who, um, a level three analyst who has to review that data manually.

[00:10:43] Um, it takes them. Multiple of the time that it took to collect that data. So you may be talking for every hour of data. It may take them two to three hours to review that data in the manner to assure that, you know, they’re, they’re seeing everything and [00:11:00] analyzing everything. So, uh, we developed, uh, we’ve been in the process of developing, doing research of developing algorithms to do that analysis in an assistant manner.

[00:11:13] And when we did that analysis in assisted manner, and it was done, uh, ba basically it was done in a manner that it was at least as reliable as the, as the individual, as the person, the human performing that, that analysis. But it saved significant time in the analysis process. Mm-hmm. Um, and which, which saved, you know, manpower.

[00:11:41] Man hours spent on the analysis. So that was the savings of significant savings. In this particular case, it was, it was about $50,000 per exam. And it also saved, um, time for the, um, end user, which was a utility. They [00:12:00] typically had oversight of the analysis, it saved them $10,000. And then it really saves overall in the, in, in the amount of time that’s spent in analyzing the data.

[00:12:11] So you’re able to analyze the data much quicker and then use the, you know, get the results of that data and be able to use those results of that data. You know, probably, you know, a day or two in advance of when you normally would be able to do that if it was being analyzed manually. So, you know, going back to your question, you know, that was an example of, of a recent success, but it’s, it’s basically, you know, looking at specific projects and showing how they can be successful and, and how they can bring either cost savings, um, be more reliable.

[00:12:47] Or also result in data being turned over and, and understood in a manner that’s, uh, you know, more quickly than you would normally have it if the, if the data was [00:13:00] being manually, um, manually analyzed. So I imagine,

[00:13:06] Nasrin Azari: I imagine that as more and more emerging technologies are incorporated into the inspection world that.

[00:13:17] That that problem or that situation is going to just explode because you’ll end up having more and more data, which really is, is, is, is unmanageable for. Right. Manual analysis. Right. Yeah.

[00:13:33] John Lindberg: Yeah. Ano another example of that, and, and you, you’re seeing that in, in, in many industries where, You know, visual examinations were performed as, as various types of inspection, either in process or, you know, final inspections.

[00:13:49] But in particular, one area that I can think of, um, is inspection of wind turbine blades where, uh, in the past that was done using. [00:14:00] You know, zoom cameras, mounting on the ground, whatever. Um, and, and taking individual photos. Now they actually perform that with unmanned aerial vehicles, with cameras mounted on them, and you collect a lot of data very quickly and, and it just, there’s, there’s much more data to analyze, to look at than there was previously.

[00:14:25] So you have to look at methodologies with, such as a, you know, um, artificial intelligence assisted analysis to help, you know, improve the, the process of, of analyzing that data. It speeds up the data analysis. It basically eliminates, you know, a lot of data that that. You don’t need to see and allows you to focus on the things that you do need to see.

[00:14:49] Yeah. So, you know, we are seeing, you know, in particular the use of, you know, artificial intelligence assisted analysis being used in, in [00:15:00] many, many types of inspections and examinations that are, that are being performed. And so I think as we go into implementing those and, and. You know, I think really in many cases we’re really in the infancy of implementing a lot of these technologies.

[00:15:16] Mm-hmm. So as we go into implementing them more on a broad scale, you’re gonna see, um, people understand the value of ND 4.0 technology. Yeah. Uh, but you know, I think you have to do it in steps. You have to show it by each use case or each project, you know, how it’s providing value.

[00:15:38] Nasrin Azari: Yeah, you, um, just to kind of, to clarify that a little bit, in your experiences, what has been the most successful way for communicating the value of ND 4.0?

[00:15:50] Is it through some of those metrics you were talking about, those kind of like value metrics or, or what would you [00:16:00] say has been, I guess, what resonates most with your customers?

[00:16:05] John Lindberg: Uh, that, that’s interesting. I, it, uh, I would say the, the metrics that, that the customers think about more star, you know, time and money.

[00:16:15] Yeah. Um, so does it, does it save ’em time? Does it save them money? And, and how is it saving them time and how is it saving them money? But, and, and so, you know, when, when you’re looking at the use cases, you kind of have to think about that. In terms of when you’re looking at the metrics, you know, this is the way we did it and this is the way we used to do it.

[00:16:40] This is the way ND 4.0, you’ll do it in an ND 4.0 methodology. So when you look at that, you kind of have to look at what the, the applicable metrics are. Yeah. But you know, for instance, the other thing is, you know, there, there’s in many cases where you can’t, you can’t necessarily measure [00:17:00] the metric. Um, with the time, the time value or the, the, the cost value thing.

[00:17:07] It may be the fact that, you know, by automating the analysis, now you’ve taken the fatigue factor off of the, the, the human, you know, the person mm-hmm. Analyzing that data and it results in by, you know, with fatigue, you got a lot of issues with human error, so you reduce the human error issues. Right. And, and you also.

[00:17:31] You know, um, provide that individual with more time now that he can focus on the things he needs to look at versus all of the things that are in the data that he really doesn’t need to be looking at. Yeah. You know, so, you know, it’s, it’s, it isn’t a simple, you know, time, cost, value statement. Oftentimes it’s, it’s other, other factors that, that tie into that.

[00:17:58] And what

[00:17:59] Nasrin Azari: about [00:18:00] one of the other things that, you know, sometimes that, that we talk a a little bit about that that sometimes gets pushed to the edges, um, is, is risk, you know, so would you say that, I mean, there’s obviously a value if you can prove that you’re reducing risk and kind of what you were just mentioning before is, is an example of that.

[00:18:22] If you’re, if, if you’re able to. Make sure that you’re, you know, make the technicians that are working more effective because they’re not as fatigued. That’s a one way of reducing risk. Um, but I’ve kind of heard stories from both sides of it that if you automate too much, there’s this fear that you’re increasing risk.

[00:18:43] You know, that maybe, maybe the AI is’ imperfect, you know, and we all know that these arguments are kind of, you know, um, Can, can sort of go in circles depending on what kind of outcome you want.

[00:18:54] John Lindberg: Well, I, I think, you know, right now how we’re looking at the implementation [00:19:00] nd 4.0 and specifically looking at the areas where you’re, you know, you know, automating the analysis or assisting the analysis through, through AI methodologies is we’re still looking at the person is making the decision.

[00:19:16] So all of the, all that the AI is doing is going through the data and, and, and basically, um, simply sorting the data in a manner that the things of importance, it sorts out that they, they require further evaluation and that the other things that are, you know, that are, are non-relevant. Or there, there’s no issues that you’re, you don’t need to look at that.

[00:19:46] You may still, you may still have a process where you’re looking at some of those things, but you’re really focused on those things that are of importance that mm-hmm. Are either, uh, what we call reportable or recordable indications or [00:20:00] rejectable indications. So you’re still relying on the human to make the decision.

[00:20:07] Yeah. So I think that’s important as far as looking at the risk, you’re, you’re really lowering the risk because you’re reducing, you’re, you’re, you’re making the analysis more consistent by, by having the analysis done by machine. Cuz there there’s been a number of studies that have been done that, you know, human factor studies that indicate, you know, when, when the human is fatigued, that’s when they make mistakes.

[00:20:34] And that’s where you get inconsistencies in the analysis of the data. Sure. And it’s not just by a single person, but it’s in between two persons. You, you take two level threes and they may, they may see the data differently. Um, and, and so that results in inconsistency. Yes. Um, so, By having the machine looking at the data, you know, analyzing the data, you know, it, [00:21:00] it’s, it’s really, it is more consistent.

[00:21:02] Mm-hmm. You know, we’re. You know what? In, in the research and development that we’ve done, we, we’ve proven that fact that we, we can be more consistent in the analysis of the data, but as I think, as we progress through ND 4.0 and you start using more of these, you know, AI assisted methods and more of the methodologies, you know, uses of augmented reality and virtual reality to, to basically visualize the data.

[00:21:32] You’re, you’re gonna see that it’s going to improve, you know, the consistency in the analysis of the data, the reliability of the analysis of the data, and reduce risk. Yeah. And the other factor, you know, the other factor associated with that, and I I mentioned before, is that by reducing the amount of time it takes to do that analysis, you’re also, you know, allowing.

[00:21:56] The results of the data to be acted on sooner. [00:22:00] Mm-hmm. True. So from the standpoint of a plant where, you know, if it’s an operating plant to get that data, you know, um, sooner, um, is, is is much better from them, for them from a risk perspective.

[00:22:16] Nasrin Azari: Right. Yeah, that makes a lot of sense. So we’ve talked quite a bit about, um, AI assisted analysis.

[00:22:25] Um, are there any other n d e four nano technologies that you think will have a, a, you know, relevant or impact, um, have a big impact on the energy industry as we move forward?

[00:22:45] John Lindberg: Um, I, I guess the, one of the biggest things I see that, and that we have to do in MD 4.0. Um, and, and we, we are working on that is to, to basically have [00:23:00] consistency in the data formats.

[00:23:02] Mm-hmm. And so, you know, you’ve got different types of, of examinations that are performed. In power plants, nuclear power plants, you know, they’re either visual examinations, surface examinations, or volumetric examinations. And when you get into the technology that that is used to perform those examinations, they vary.

[00:23:25] So the data formats that, that, um, for the results from those inspections differ. Mm-hmm. When you get into automating those inspections, how you collect the data, Um, they differ and they also differ by, um, the vendor or the equipment maker, uh, the equipment vendor or the equipment manufacturer or the vendor that’s performing the inspection.

[00:23:54] They may have proprietary software that puts the data in a [00:24:00] proprietary format. So one of the problems that, that we have and, and. Uh, one of the, the biggest things I think we need to overcome is getting some sort of commonality with the data formats so that you end up with a d data format no matter what type of examination.

[00:24:19] It is a visual examination or volumetric examination where you can convert that data into a format where you can visualize that data. You know, through, you know, augmented reality, virtual reality, um, you know, various types of methodologies feed that you know, back into possibly a digital twin, such that, you know, from an operations standpoint that operations management can look at that the data is now is in a format that they can look at and understand and be able to make decisions from.

[00:24:53] So I think, you know, one of the, one of the biggest challenges we have right now, um, and one of the things that we [00:25:00] need the most is, um, commonality with, with the data formats. Yeah.

[00:25:05] Nasrin Azari: Sounds like some standards are required. We’ve heard that quite a bit too.

[00:25:08] John Lindberg: Yeah. Yeah. And it’s, um, you know, and, and I guess you look at.

[00:25:14] You know, various industries, but I, but I, I, you know, I think of, you know, video formats and, and through, through the years, how you’ve had different video formats Yeah. As you went through, you know, you had, uh, beta Max and, um, VHS video tapes, and then, you know, you went to CDs and DVDs and, and as, as all that moves along, you know, There, there, there gets some commonality in the format, but, but it’s still, right now the problem we have is with the proprietary, um, software that vendors, equipment vendors or inspection vendors may use to collect the data and then, you know, [00:26:00] analyze the data and visualize that data.

[00:26:02] I do think, um, and, and. I’ve seen and heard more of this over the last. Two to three years as we’ve been talking about ND 4.0, more discussions around how we can, you know, uh, come up with common data formats. Yeah. And I think it’s, I think that’s gonna be one of the most important things that we, we can do in ND 4.0.

[00:26:26] Um, to, to really, to really push it ahead. Yeah, I

[00:26:29] Nasrin Azari: agree with you there. And we’ve been involved in some of those discussions too, as a platform for n e companies and in that data management, um, segment of the market. It’s definitely a problem that that needs to be resolved. Um, and I, I guess finally, uh, today we’ve touched on some of these things.

[00:26:48] You just talked about standards and data formats. Um, we talked a little bit about. Culture change. What would you say are the biggest hurdles to N D E four point adoption [00:27:00] specifically for the energy industry?

[00:27:04] John Lindberg: Uh, well, I think the energy industry, like any other industry, is driven by cost. Um mm-hmm. And, and, and certainly the cost of power, so, Um, typically when you, when you look at N D E and the power industry, and, and my experience has been mostly in the nuclear power industry where examinations are regulated by federal law, they’re required by federal law.

[00:27:31] Uh, but you know, when you’re doing those examinations, there’s a cost associated with doing those examinations. So, Everybody. Um, I, I believe everybody outside the ND community and probably including the ND community views N D E as being a cost, um, you know, uh, a cost factor or a cost. Mm-hmm. You know, cost center.

[00:27:58] Yeah, a cost center. [00:28:00] Um, and so what we’ve gotta change is that paradigm and. And it, it’s going to be, it’s gonna be difficult to do, but the way we can change that paradigm is by showing, uh, like, like I’ve talked before about, you know, the automated analysis, you know, just collecting the data in an automated, in a automated manner, having recorded data that you can go back and look at, compare, compare previous data with current data, but being able to use that data and, and have value to that data.

[00:28:37] Yeah. Um, is, is really going to, you know, bring it from being a cost center to being a profit center.

[00:28:45] Nasrin Azari: Yeah. I love the idea of, of being able to take this information and there’s so much information that’s being collected and analyzed and discovered from data that’s being collected and using that to become more proactive [00:29:00] versus.

[00:29:02] You know, it seems like inspections are kind of like a checkbox today, right? Right. And it’s just, and a time consuming one, it takes so much time to get it done. And like you said, you know, if you could reduce that time, you’re providing value. But if you could, if you could also kind of do some of that trend analysis you’re talking about, um, and sort of bring inspections more into the, the life cycle of assets versus just kind of like, On some kind of a schedule.

[00:29:32] Right,

[00:29:32] John Lindberg: right, right. Yeah. Well, and I, I think you, you, you know, you brought out a good point and I think, you know, one of the things, you know, in particular, uh, in the nuclear industry, um, what happens, we, we may do automated examinations and we may do automated analysis, you know, starting to do automated analysis, but still what becomes.

[00:29:55] The, the document of record for any inspection or any [00:30:00] examination is done is a, basically a manually created document. Mm-hmm. You know, either a handwritten or, or something that’s been generated by computer, but that is the document that goes into the record. So when you go out and do a subsequent examination and say you come up with different results or a new result, You go back to that, that handwritten document.

[00:30:25] Well, that handwritten document really doesn’t tell you anything except it was either acceptable or we found, you know, we found something that was of interest. You’ve gotta go back to the actual examination data and if it was done as an automated examination, um, then you’ve got recorded data, then you can go back into that recorded data.

[00:30:46] But all of that takes time. Yes, it totally does. You know, basically, You know, the, the, right now the data, the, the data serves as historical record that an examination has been done, [00:31:00] but it’s not factored into being, you know, used for monitoring that asset in terms of its life cycle, right? So that you can easily pull it up, you know, if something comes up, you can pull it up and compare it with.

[00:31:15] Operational data. Okay. We had an, you know, an operational perturbation, you know, um, higher flow, higher temperature, higher pressure, you know, and that may have caused an issue, you know, and, and so you can’t compare the examination data, the ND data with any other data. And that’s where we, we need to get to, is to be able to, to use the ND data.

[00:31:39] As, as being, having value with all the, the other data that’s collected for all the components and all the assets that, that are in the plant.

[00:31:49] Nasrin Azari: Yeah. Yeah. That’s kind of the dream world, I feel like is, you know, sort of bringing the inspection process into the fold and like you said, [00:32:00] making the data more usable, more valuable, and the more you do that, the more valuable it will be in the future.

[00:32:06] It’s just this. You know, great. Um, you know, there’s great potential there. So, um, well, I’ve been through all of my questions. I did wanna, um, open, open up a, a sort of, provide an open question for you to see if there’s anything else you wanna share before closing today. I know you’ve worked on a lot of projects within the energy industry, and is there anything that stands out that we didn’t talk about today that you think might be interesting or helpful to our listeners?

[00:32:36] John Lindberg: Well, I, I think, I, I think the thing that I think would probably be most helpful to our, our listeners is really looking at, you know, from the n d perspective, looking at how you, excuse me, um, how you can automate, um, the acquisition of the data. So that you end up with [00:33:00] recorded data and then once you’ve got that data recorded on the media, there’s more things that you can do with it in terms of automating the analysis or, you know, even if you’re not automating that analysis, you’ve got recorded data that you can go back and look at, you know, in the future, you know, when you’re doing a subsequent examination.

[00:33:21] So you can do a comparison of the data. Yeah, I, I see the future. I see the future and, and this is kinda, you know, the future, future, but it may not be too far out in the future. We talk about digital twins. Yeah. And you know, I, I think, you know, you’re hearing more and more about digital twins and I think as we move into the use of digital twins for any and number of industries, not just the electric power industry, but any place where you’re, you’ve got components and you’ve got.

[00:33:57] You know, assets to manage and, and you’re trying to [00:34:00] manage the life of those assets. I think digital twins is gonna play a key part into this. Yeah. So that you can input all of the data that, that is pertinent to that component and, and to those plant assets. And, and you can easily pull up that data and, and, and look at all of that.

[00:34:22] Look at all of the data, you know, more easily, more readily, more quickly and, and make better decisions from that data. And I think by doing that and having that ability to do it on a sort of a, a continuous basis, like a monitoring type basis mm-hmm. Versus periodically doing inspections, you’re gonna be able to monitor that plant and you’re gonna end up lowering the risk associated with.

[00:34:48] You know, the safety risk and the risk to equipment failure. Um, and, and it’s gonna lower costs. Yeah. So, you know, I, I think in, in the long run, we’re, we’re gonna get there. It’s just gonna take, [00:35:00] you know, steps to, to, to get that and realize that, yeah, it takes time. I do think, mm-hmm. Discussions that we had internally, You know, we, we saw this, the signi, we see the significance of and the importance of the digital twin and moving that into, you know, how you’re monitoring the asset in the future, but we don’t understand how we, we don’t fully understand how we get to that point yet.

[00:35:28] Yeah. You

[00:35:29] Nasrin Azari: know? Yeah. It is fascinating to think about if you had an operating digital twin and you could do. Scenario analysis. I mean, that to me is really exciting. That way you, you become, you, you’re predicting what could happen, right. Versus, you know, just trying to prevent, right? You’re predicting and then you’re taking steps to avoid.

[00:35:52] And that to me is really, you know, sort of nirvana for the industry.

[00:35:58] John Lindberg: Right? Yeah. Well, and, and I [00:36:00] think, you know, many, many industries, Need that sort of Yeah. Approach. Crystal ball. Yeah. Yeah, the crystal ball. I think that’s what everybody’s looking for. But yeah, you know, it, it, it’s just gonna take time to get there, but I think, you know, industry 4.0 and ND 4.0 is really the, the way that we’re going to get there.

[00:36:24] So, yeah, I

[00:36:24] Nasrin Azari: agree. I agree. And, um, yeah, it’s very exciting. So I was, I was pretty sure this was gonna be a great discussion today. And you certainly exceeded my expectations, John. So thank you so much for participating and being here today and bringing such an interesting and knowledgeable perspective. I really appreciate your experience.

[00:36:44] Okay, well thank you. Um, I encourage our listeners to follow John and or EPRI to stay up to date on the interesting projects they’re involved in. We’ll put post links on our podcast webpage so that folks can find you, John. [00:37:00] And, um, thank you to our listeners for tuning in today. If you have any feedback or would like to nominate an individual or an organization to be a guest on a future episode, Please send a message to me directly at N A Z A R iLIGHT soft.com, or you can submit the contact us form on our website, www doof.com with your contact information and we will be in touch.

[00:37:23] Thanks again for joining us and see you next time. Thanks,

[00:37:25] John Lindberg: John. Thank you.

[00:37:39] To learn more about NDE

[00:37:41] Nasrin Azari: 4.0 emerging technologies and digital transformation, please visit www.floodlightsoft.com for additional resources, including our blog and several relevant white papers. If

[00:37:53] John Lindberg: you have any questions about today’s episode or suggestions for future episodes, please send an email [00:38:00] to info floodlight soft.com.

[00:38:02] Thank you so much.

For more expert views on NDE 4.0, subscribe to the Floodlight Software blog at floodlightsoft.com.

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