We spend a lot of time, energy, and money capturing the current condition of an asset to calculate the remaining safe use of the equipment. All the measurements we take only have meaning though, if we know the original dimensions of the base metal and that the metal itself was manufactured to exacting tolerances.
The accuracy of our work can be greatly affected if we don't have an accurate starting point. For example, if I'm performing UT on a tank floor indication and it's showing me the remaining metal thickness of 171 millimeters, it will mean very little without knowing the nominal, or the original thickness of the metal. If our base metal was originally a quarter inch or 250 millimeters, then we can easily establish a corrosion rate from these two known points and evaluate the time it will take for the asset to fail.
But what if that plate was manufactured wrong? What if it started 15 millimeters thicker or thinner than what we're anticipating? There's a pretty good chance that this will affect the outcome of our calculations. Fortunately, there's a whole segment of manufacturing quality control ensuring that we have an accurate starting point. These folks are using a lot of the very same tools that we're using on the backend to capture the current state of infrastructure.
Jason Wilburn of Foerster Instruments operates on this bridge between NDT and manufacturing. Jason is the President of the North American division of Foerster, responsible for developing and manufacturing, instruments, and systems for the NDT enhanced quality control. I chatted with Jason on the latest episode of Route to Reliability to get his insight on NDT, manufacturing and the future state of the industry.
Below are a few excerpts from the conversation. Listen to the full episode here!
Let's start with how you found your way into inspection and reliability.
Like a lot of people in our industry, my path was a little unconventional. I studied sociology as an undergraduate. My professor was teaching the cultural aspects of the relationship between the American automotive industry and the Japanese automotive industry. I was introduced to industry, quality, Deming, and some of the players in that space.
I became obsessed with manufacturing and after I graduated, I started working in manufacturing. I started out actually in sales, which wasn't fulfilling for me at the time.
I ended up working as a process engineer and working as an expediter on the floor, then I attended Penn state University and got a degree in Quality and Manufacturing Management.
That's where I was exposed to the statistical process controls and the different techniques, the real nuts and bolts. After that, I spent some time bouncing around to a couple software startups.
Then, I got a job at an industrial services company that did Eddy-current testing, and non-destructive testing on tubing in heat exchangers, and condensers.
Not many kids grow up wanting to clean and test condensers and heat exchangers, but it's a fascinating world, once you get into it and start to understand the impact that you have on the world.
It's easy when you're searching for a career with purpose. You can wake up every morning and say, "if we do this right today, we're going to save some lives."
That is an absolutely unusual path, but it sounds like you've got an awesome cross section of industries.
I always bring a different perspective to the business. Most NDT professionals will tell you that half of it's the data and half of it's how the organization really feels about quality. Do they invest in their assets and manage them or do they just run them until they break? You have to identify that. Those are organizational cultural issues, not technical issues.
Now the Forester Group straddles a lot of different verticals. What would you say the specialty is?
Our tagline is, "we make quality visible." In the United States, we've sort of picked up our own little motto and it's, "we find things before they ruin your day."
In that vein, we have four major business units and the first one was a series of handheld devices that are used for mine detection and for finding unexploded ordinances. So yeah, that could ruin your day.
You want a quality instrument if you're playing that game.
Exactly. So that's how we got started. The founder, Dr. Forester, that was his expertise, using the magnetic field to identify unexploded bombs underneath Germany after World War II.
From there, we got into Eddy-current technologies and focused on providing a variety of techniques for identifying surface, flaws, and defects in steel traveling at production speeds.
Then we got into component testing, primarily for the automotive and aerospace industries, finding surface defects and cracks.
Is automation taking up a bigger portion of your business in these processes?
Our group in Ohio is, which we've recently started to call the Integration and Automation group. We do a lot with robot cells and automated inspection, especially in component testing.
We've noticed more sophistication from the customer base as well, talking about integrating into the big brother systems and making sure we're collecting and sharing the data. They're talking about NDE 4.0, Industry 4.0, Internet of Things.
Marketing is usually way ahead of the reality and that's just in every industry but we're starting to see how our customers are taking it seriously. They truly want to integrate the results from the non-destructive testing into their overall processes.
We've had some excellent conversations with guests about Industry and NDE 4.0, could you flesh that out a little bit? How would you define the end goal or driving force behind this new program?
Sure. I might have a slightly different take on it than others but it really depends on your perspective.
It's digitization. It's process. It's robotics. It's a little bit of everything.
I go back to my social science roots and my fascination with Deming. At the end of the day, it's all about making faster, better, smarter products and making jobs easier and saving more lives. If it doesn't do that, we don't need it. I think we're still trying to figure out as an industry what the components are.
I know exactly what you mean and speaking in the context of oil and gas, it's not just the data but it's receiving the data again. We're taking measurements, but we're using those measurements in a go no-go context. Rather than, now that we've got all these aggregate measurements, what can we conclude? Is this a seven-year fix? Or is this a 10-year fix?
It's not just the data collection but it's very much a matter of being able to digest that data and make good decisions based on it.
Yeah. When you're looking at tank life, for example, there are a lot of variables that impact tank life, but they're not unknown. We're digging into that and getting more complete data, to form forward projections.
The same thing applies in a manufacturing environment. There's a lot of study and research done, before, during, and after a lawsuit or the failure itself. Even a large shipment of subpar product. What can we do upfront to figure that out?
In the oil and gas industry, we work a lot with pipe. More or less, they'll run the pipe through our system, and classify the pipes quality based on surface condition.
Some would say, "well, what if we could just make more high quality pipe? And what if we can use the testing data to go back and look at other things?"
If you're doing it after the fact, you've already made say, thousands of tons of similarly bad pipe. Right? We need to be proactive.
It's technical, it's cultural, and it's a lack of technical talent. We need to find people that have the right background and want to look at pipe data all day.
You're right. These systems are generating a greater need for a special kind of person that can scrub that data or the AI systems that are going to be able to do this on their own too.
It's getting there. Even in the 13 years since I've been in that field, it's come a long way. We're making huge progress and it's really interesting.
On that subject, with your view of the industry, what kinds of technologies do you see coming to the forefront? And what do you feel is really going to define NDE 4.0 in the eventual future of this?
NDT techniques like Phased Array, Eddy-current and now E-Mat, are starting to improve the things we can find.
Now, we can find oblique cracks and defects in a piece of tubing. The specs don't call for finding these yet, so maybe in the future, API will make it a requirement.
Also the acquisition of the data-- we're doing much more consistent testing and it's making comprehensive data affordable.
That's the foundation you need for Industry 4.0 because machine learning without human intervention lacks context. Sometimes technologists try to take the people out of it and you just can't. There's people involved in these processes and you need to understand them.
Was that data collected on Monday morning or Friday afternoon? You need to know those things. And machine learning is not going to figure that out until we have really consistent data acquisition, both in the field and during in-line manufacturing data acquisition. There's gotta be that desire for consistency.
Exactly and just to summarize, it sounds like you really believe that it's all going to start with the data.
Especially in the oil and gas world, if we can increase safety, reduce the amount of people on site and improve the environment, it will lead to a better data pool. That data pool will drive what comes next.