Podcast: Oil analysis can boost equipment reliability, but only if you use the data

In this episode of Great Question: A Manufacturing Podcast, chief editor Thomas Wilk talks with Mike Holloway, president of 5th Order Industry, about the intersection of artificial intelligence and oil analysis. Mike has more than 40 years experience in industry and holds 16 professional certifications, a patent, an MS in polymer engineering, a BS in Chemistry, and a BA in philosophy. Mike is a subject matter expert in tribology, oil and failure analysis, reliability engineering, and designed experiments for science and engineering. He is currently publishing a blog series with Plant Services on AI and oil analysis, and previously published an article on tribology certifications with this magazine.

Below is an excerpt from the podcast:

PS: The article that came out of our initial contact was one on certifications in lubrication, but you're writing a new blog series for us. It's a multi part series and in the first installment you talk about oil analysis customers who do and don't use those data to improve reliability. Maybe we can start there because I was struck by one of your statements, that when someone asks you how many customers use those data for the oil analysis companies to improve reliability, you put a thumbnail on that of about 15%. I was curious about that number, what are they doing right and what are the other 85% doing wrong?

MH: This really came about because for a short time I worked at SGS, and my boss who passed on recently (his name was Patrick Runchey), actually, two days before he died of an aneurysm, he called me up after I'd been working for my own company. And he said, hey, I’ve got a question for you: How many customers of ours do you really think use oil analysis to improve reliability and whatnot? I said that pretty much it's between 10-15%. 

He's like, no, I don't believe that, it’s got to be more like 50%. I said no, Patrick, I know for a fact because of all the years I've done training, a lot of times I ask this question to all end users and it's about that. He's like, that's ridiculous, why do we do this then? I said, we do it for the 10-15%. He's like, is that good enough? I said, well, that's a question you have to ask yourself; to me .oil analysis is an exercise bicycle. He said, what do you mean by that? 

I said: listen, exercise bicycles have been around as long as bicycles have been around. They really have been. But the interesting thing about it is that they're not a really good business, because the idea is that you buy the bike, you use it for about a month with all great intentions, and then you just kind of forget about it. We saw this with Peloton, and that was a high tech exercise bicycle. But it really failed, and the reason is that people don't make it a habit, and they don't see the immediate benefits from it. And that's always been the problem with so many things. To transition into the series about AI, I talk a lot about the innovation of AI, but we see a lot more immediate gratification and influence through AI than we do through other things. When we actually do take a look at oil analysis, it could take months if not quarters, if not a couple of years to really see the overall benefit that this thing brings to the table. 

Many years ago I worked for ALS and we had a really large account – Wal-Mart – and they're pushing maybe 35,000 samples a month, some ungodly number. And I was watching the numbers and all of a sudden one month nothing was coming in. So I called the account manager and I said, can you look into this? Why haven’t we gotten any samples in the past week from Wal-Mart? And she did, she called back and said, I can't get through to the decision-maker. I said, well you’ve got to, something’s up. She finally did and said they just decided they didn't see any value in it anymore. I go, why is that? She said, everything's green, everything looks good. I said, ok, however, there's still value in that. She said, apparently they just said, “hey, we don't need this anymore, because everything's fine.” 

I said, you know, that's analogous to when we go to the doctor for an annual physical and the doctor says, you look great, keep on doing what you're doing. Then you think to yourself, do I have to go next year? Everything has been fine for the past 20 years, why should I go next year? Well, that next year, if you don't go, maybe something's not going to be fine. You don't know, it’s really to keep an eye on things, but people don't realize that. They just think that if there's not a problem we’re just going to move on and attack only problems, because human beings are really good at solving problems and innovating, we're really great at it actually. It's probably one of our only things we're good at, and it has to do with our ability to communicate and work as a team.

What's interesting is that with oil analysis, is that it’s a cool idea, you can't find anybody that disagrees with it, but you can't find many people who embrace it to that degree. Now, that's not necessarily always the same with like thermography or vibration analysis. Because with vibration analysis it's a physical thing that we can touch a machine and feel it shake a certain way; or with thermography, we know if it’s hot, it's not right. Those are all physicalities but oil analysis, the best you can do with that is maybe smell the oil, and if it smells acrid or if it looks really dark then you could have physicality to it, that maybe there's something wrong with it. 

Normally oil is like blood – you don't touch it, you stay away from it. You can analyze it remotely or through a lab, but you don't have that visceral feel for if it's any good or not. Therefore when we don't have that, I think we lose our ability to truly understand the value of it. People don't understand an atom or a molecule. They just conceptualize it. So how can we expect them to really understand the nuances of used oil in order to bring to the table?

PS: You're putting your finger on something – the human element of the kind of folks who go into this field. I hadn't thought about it this way, that the people who like getting their hands on the machines and their hands on the equipment, they like tactile, sensory data. And what you're saying here is when it comes to oil analysis, some of these people, if it's not tactile enough or if the results come back positive all the time, there's a tendency just to put those data in the background in favor of the more immediate: “I can hear it, I can feel it. I can smell it.”

MH: Sure. And there's a distinct reason why, and it has everything to do with how we think and how we learn. Years ago when I started my company, I realized that by presenting and teaching and instructing in person, a lot of times the reason why it's so effective is because I could tailor my approach to my crowd. If I was in a place that had mostly technicians and millwrights, I would get a lot more kinetic, I would put things in their hands. If I was with a bunch of engineers, there'd be graphs and tables and visual things. If I was teaching sales folks, it was all auditory stories, they're very auditory. 

I got to thinking, everybody's different but really not that different. If you think about it, there's probably about 27 different types of characteristics of a personality. But when I start to look into it, there's several different ways in which people learn, and there's a dozen and a half types of intelligence. Now, you've probably heard about this, “oh this guy's got street smarts,” or “he's really logical, he's not street smarts.” But then there's also those that have kinetic smarts that are incredible athletes, but are horrible human beings. There are also those that are great musicians, great mathematicians, there's also people that have an intelligence for dealing with other people or being introspective and understanding themselves. 

So I started doing some research into that as well, and I found out that yes, there's about 8 different ways in which people learn depending upon your neurology, and there's probably about 18 different forms of intelligentsia. (I actually started finalizing a book on different forms of intelligence on how to improve what you don't have and take advantage of what you do.) But when I built my company, it was really predicated around customized training, and I even did that online, where you could go online and take a customized assessment to find what kind of learner you are. And then if you want to, I can actually build a course according to your learning modality. 

Well, it comes into play if we think about this oil analysis too, because the people that we're dealing with are mechanics, technicians, and millwrights. They have a propensity to work with their hands. If they weren't, if they're more visual and auditory, they would have gone into a different profession. The people that I deal with (mechanics, millwrights, and technicians) don't like sitting in a classroom. They like getting their hands dirty doing something, you know? And so it makes sense if they can't understand an oil molecule or a test result because it's not physical for them, they're not going to gravitate towards it. They will go towards vibration analysis, because that's physical for them. Now, if you take a look at vibration analysis versus tribology, it's really completely different, and quite frankly vibration analysis to me is like dark magic or something. It’s really complicated, but it makes perfect sense to many of these guys. 

To me, I like the more esoteric abstract. I can completely understand chemistry, it’s like second nature, so it's not so abstract to me. But with oil analysis, if it just doesn't fit, then it doesn't gravitate towards them, unfortunately, because the way their brains are built.

PS: You mentioned in the first blog post that portable oil analysis units that you can use within factories and mobile equipment, that's getting closer within reach. Do you think the closer we get to those kind of technologies being available to everyone would make a difference, if these technicians get their hands on them?

MH: Yeah, absolutely. Back in the day, maybe 100 years ago, if you were driving a vehicle, it had a few different gauges and people who really started getting into automotives really had a certain sense of what the engine sounded like, and had a feel for it. It wasn't until many, many years, decades later, that we started getting warning lights and indicators and gauges that respond to what's going on, that we get a true appreciation for the reliability and performance of the vehicle.

It's the same way with machinery. Gone are the days (or will be soon) of the old mechanics that are just touching machine and telling you what's wrong with it. Now, you have your PLC and it’s telling you exactly what's up and what you have to do about it. I think in the first blog, I talked about a project that I worked on many years ago and it was for the Joint Task Force Fighter, the F35, to build a very small oil analysis unit that could test on the fly if the oil was good, cautionary, or bring the bird home as fast as possible. Part of the specification was, and they left it up to me to say what do you think we should look at as far as parameters and sensor technology, was that the footprint is 2” by 2” by 6”. It's not very big. We had to miniaturize a lot, which was interesting. 

I said, how do you want the indicator to be? A gauge or something? “No, -- three lights: green, yellow, and red. That's it, and we'll put it on the IP and it will just say Oil Condition Indicator” or something.” When you're a fighter pilot, you’ve got too many things to look at. They just want to keep it simple. I said, OK, that's great. 

Now, if we have that with machinery, like with an air compressor or hydraulic press, or for that matter a haul truck engine that says, hey, we’re approaching some problems, we're going to yellow, so bring it back into the state and we can do a quick diag by plugging a USB port into the undercarriage and then boom we're good. This is exactly the way we're going now. In the past, we never really saw on-board oil analysis really become mainstream because of cost. 

Years ago, GM did have something along those lines where they said “you have so much oil life left”. Today, you and I, when we drive our vehicles, it'll tell you if it needs an oil change, but it has nothing to do with oil condition. It has to do with engine revolutions. It just counts many millions of revolutions and they assume that at some revolutions the oil is going to be burned. Not necessarily, but they're going to take the safe way out and say I should change the oil, better safe than sorry. But GM did have a sensor technology because I remember meeting a woman who came up with it, and it was really interesting. It actually tested the effectiveness of an antioxidant package that's found in all oils, and as soon as that went down to a certain level – it was a form of sweet volumetry actually – it would indicate that, hey, listen, our antioxidant package in our oil is no longer any good, or it's going to really start to become challenging, so it’s time to change it all. 

That was really the first thing, but it didn't really catch on and it didn't provide great tech in terms of being repeatable, reproducible, accurate, and precise, but it was a great step in the right direction. I wish I still had her business card because I'd probably give her a call, find out what she's been up to on it. But they kind of just forgot about that, nobody really went back to it because they didn't have to. They felt, you know what? We can come up with a better idea by revolutions and just say, “through our development work on the very front end, we know that it can only last in a box for so long. We're going to assume that that's what it’s going to be like with engines going forward, and just say, change the oil at 10,000 km or 200 million revolutions or something like that.” 

Is that true? No, I mean seen oil last in fuselages 100,000 miles without being changed. I know the Navy, they don't even change in the boat, sometimes they don’t even change the oil. They just add to it and filter it, and that’s it, the never change the oil. I think going forward that's probably the direction it’s going to go with most engines, if we stay with combustion engines. You won't have to do it, just change the filter and add oil, and that’s it. Will you need oil analysis for an engine? No. Should you have this for something like a power turbine or hydraulics? Yes, absolutely. But for transportation stuff, it might not be something that someone's going to gravitate towards because of expense. 

However, now we're starting to see the cost of these sensors go down dramatically. And what's also interesting is let's say you have a sensor device and it could tell you if you have a certain amount of water in there, a certain amount particulate, a certain amount of wear debris, maybe even give you indication of your oil condition, acidity or neutralization capability, or even anti-oxidant concentration. That's all really good, but where does the data go? That goes down to your instrument panel and tells you, well, you're allowing a customer try to figure that out? You’re not going to do that. Well, then you just do the red, yellow, green, and then bring it in for a diag at your local mechanic. 

Then we have the concept of, what you do with the data? I worked on some projects where we could actually take these sensor boxes and send that information to the cloud and then it goes into a bank in there for diagnosticians to assess what's going on and then make a recommendation, and that took time. We're now looking at saying, well, what if we just really threw that over to Chat GPT? I've read some of these reports and they looked fantastic. A couple years ago I said, who wrote this report, and one of the lab managers said we got a ChatGPT version of what we have internally. I said, that's amazing, it probably did it instantaneously, you could convert everything to that and probably put 250 diagnosticians out of a job within a week. Then he kind of looked at me.