Welcome to the Trend Detection podcast, powered by Senseye, an industry leader in using AI to drive scalable and sustainable asset performance and reliability. This is a new publication designed to help you go away with ideas on how to achieve maintenance efficiencies.
For this 3-part series, I’m joined by James Bond, an individual with over 25 years of experience in maintenance, condition based monitoring and predictive maintenance.
In the first episode of this series, we discuss how James got started as a maintenance professional, the key differences between the most common maintenance approaches and why culture plays such an important role in a successful strategy.
Key topics covered (click to jump to the section)
- The differences between the common maintenance approaches
- Shifting from a corrective maintenance strategy
- Examples of good and bad data collection
- Improving PdM with maintenance data
Niall Sullivan, Senseye: So, first of all, I'd like to say hi to James and welcome to the Trend Detection podcast. How are you today?
James Bond: Well, I am doing fine. How about yourself?
Niall Sullivan, Senseye: I'm very good. Very good to be speaking with you today.
As I ask all of our guests, I think it would be a great place to start for you to just introduce yourself briefly to our audience.
James Bond: Okay. Well, my name is James Bond. Yes, really. That's my name. Or as people like for me to refer as Bond, James Bond.
I've been in the predictive maintenance field for roughly around 25 years. Actually this month is 25 years that I started doing this. Have several different certifications, including vibration analysis, which I'm Category 2 certified, certified ultrasound Level 2, thermography Level 1, MLA 1, and also in my motion amplification as well.
Niall Sullivan, Senseye: Fantastic background there. You said 25 years experience, so that's a lot of experience. It'd be great to wind back the clock and maybe explain how you got started in the maintenance field as a maintenance professional.
James Bond: Well, I kind of got started a little different way than what you would call a typical maintenance professional. I started turning wrenches in a family-owned business in my hometown. But I was turning wrenches on cars rather than on industrial machines. So I have a strong mechanical background.
So whenever I was introduced to the industrial side of things, my whole life changed. I mean, wow, look at all this stuff.
So after taking advantage of a few open doors, I finally started as a predictive maintenance technician through the in-house training, by other formerly trained people. I learned oil analysis, vibration analysis and a little bit of tomography.
Years down the road I was sent for more formal training and earning several different certifications. 25 years down the road, now here I am. When I started, I never would've imagined being who I am today.
Niall Sullivan, Senseye: This is interesting. So what types of certifications do you hold?
James Bond: The ones that I've listed. I'm a category two vibration analyst, the level two ultrasound, level one tomography. I'm an MLA one, which is in lubrication. I was certified in motion amplification, which kind of ties in with my vibration background.
Niall Sullivan, Senseye: Which one would you say is better to have in terms of progressing your career, to have the certifications or to have the industry experience, or is it a mixture of both?
James Bond: Well, I guess when I looked at this, I thought about it as that's almost like the chicken or the egg. Can't really have one without the other. So, industrial experiences definitely helped me when it came to earning my certifications, because I had a better understanding of what was going on. But I feel like in today's world certifications are a must. They open doors and which gives you the experience to build on. So in my case, I was quite the opposite.
Niall Sullivan, Senseye: And so throughout your sort of longer distinguished career, let's say, how has maintenance changed throughout that period, from then to now?
James Bond: Two things come to mind. I say technologies have changed and mindsets.
Right now I'm focus on the technologies. Technologies have changed. The improvements to the predictive maintenance tools that I learned to use to the new technologies doing far more than what I would've ever imagined starting 25 years ago.
My predicted tools back then are the dinosaurs of today. It's kind of like the crank on the phone. Crank it up, get it powered up. That's kind of like what our analyzers was like back then.
Niall Sullivan, Senseye: No, sorry. I interrupted you. So all I was going to say is, how have new technologies impacted maintenance? Predicted maintenance is one area, but other technologies as well. Feel free to expand on that.
James Bond: Oh, okay. Well, technologies back then, I was kind of limited on what we had available for us, or what I had available for us, compared to what we have now. 25 years ago, say for example the motion amplification. The motion amplification, that was very unheard of.
And now we use motion amplification to help us troubleshoot and be able to determine or detect an issue that we cannot really quite pinpoint say with an analyzer or something like that. We can actually visually see the motion of the machine.
Niall Sullivan, Senseye: Yeah. Oh, so that's what technology has allowed you to do.
James Bond: Yes. Well, and then plus being able to learn how to incorporate many different technologies. Say you cannot quite pinpoint what's going on with one predictive maintenance tool, so you have to use another or another. In other words, to help support what you think is going on.
Niall Sullivan, Senseye: So what are those examples of technologies you're referring to there?
James Bond: Well say vibration analyzers, ultrasound, oil analysis. Oil analysis I've found to be a good one, especially with the lubrication side of things, because that's what I started out doing whenever I started in predictive maintenance field. And from what I had learned with the oil analysis, being able to see exactly what type of wear, how much wear and everything like that by viewing it through a microscope.
Whenever I had my formal training with vibration analysis, one kind of supported the other. Okay, I see this. Let's say for example, I did an analysis on a gearbox. Okay, of course gearbox is going to have wear, so what do I do? I back it up with oil analysis to see if they had solidified what I was seeing.
Niall Sullivan, Senseye: Yeah. So they're almost providing layers of context. Would that be one way to put it?
James Bond: Yes.
Niall Sullivan, Senseye: You get a certain level of information from one, but as you add the other layers on you begin to see the full picture of what's happening.
James Bond: Yeah. One just kind of supports the other.
Niall Sullivan, Senseye: Yeah, no, absolutely. And also as maintenance has changed over the past what you say, 25 years, there's also sort of different approaches as well, which we sort of talk about a little bit on our website as well. So, I was interested to know what you think are the key differences between a corrective preventative condition base and predictive maintenance techniques?
James Bond: Well, I say the mindsets really have changed. We've gone from a reactive type maintenance or preventive maintenance, to a mindset of being more proactive. So that would be your condition based monitoring or your predictive maintenance.
As far as corrective goes, corrected the first thing that comes to mind is fix it when it breaks. And that's a mindset that we're trying to get away from or working to get away from. And I have had quite success with that.
Granted, corrective cannot be avoided sometimes. There's always going to be that one catastrophic failure that everybody hates, but it's going to happen.
Now, the preventive just kind of brings me back to periodic maintenance, every 5,000 miles 'Hey, let's change the oil on the car'. Why? Cause that's just what we learned.
Now with condition based monitoring and also with predictive maintenance, you can use the technology that can help you stretch out the intervals of those PM based maintenance. And it also helps with the corrective based maintenance to where, okay, we're not going to fix anything unless we're actually seeing something, instead of doing it over a time base.
Niall Sullivan, Senseye: So is there an argument to have to do elements of maybe not corrective maintenance, but to combine a lot of these strategies? I don't have to work in isolation. You can still have a preventative maintenance strategy, but also have a predictive maintenance strategy. Is that fair to say?
James Bond: Yes. Well say for example, we have a preventive maintenance work order that says 'Hey, we need to grease a bearing every 60 days'. Not every bearing has to be greased every 60 days and some do, so by using say a predictive technology such as ultrasound, including that with that PM, what we're able to do is be able to say 'Okay, yeah, I don't think it really needs greasing at this time'. And after a period of time 'Hey, let's just stretch the interval of this greasing of this bearing on out', but not discluding the predictive maintenance, kind of including them, kind of making them work together.
Niall Sullivan, Senseye: Yeah. And that's really interesting. What was something you said before about a culture shift? So I guess 25 years ago, it was very much corrective or reactive maintenance. Has it been difficult to shift that culture away from that traditional approach to more advanced approach as you see today?
James Bond: It definitely takes time, but you have to. I found where I was more beneficial was to be able to make believers out of people and be able to work, say, with upper management. If you could make a believer out of upper management, everything's kind of trickles down to the technicians. So here we are making believers out of everybody.
So it also helps us, well helps me whenever, say for example I see a new technology. Okay. Here's how I've helped you in the past. Now here, what we're going to do now, just because of the technologies that are available and how I've helped you out.e are producing, that's all goodness. That changes my future. It makes my life easier.
Niall Sullivan, Senseye: It's interesting. You mentioned about, cause we at Senseye, we talk a lot about stakeholder management and important about getting different types of stakeholders engaged. So it's not just the maintainers on the floor, engineers, it's actually getting the C-level and everyone bought in to the idea really.
So how difficult has that been from your experience? Throughout your career, I mean, in terms of engaging the C-level in this shift in culture, I guess.
James Bond: Okay. Well, we'll go 20 years back. It was fairly difficult and it was, like I said, you have to make a believer then say, for example, you find an issue that saves a company so many thousands or even millions of dollars. And then you use that as your leverage. So, being able to take that and use it as your leverage, and then you could kind of show it to them and said 'Hey, look at this. This is something I showed you and showed you what was going on. And you addressed it in this timely manner. Now look at downtime that it was avoided'.
Once you start throwing pictures and then throwing dollar figures out at them, they kind of opens their eyes and going 'Oh', but you have to be very consistent with it or it fades away, loses its luster.
Niall Sullivan, Senseye: Yeah, no, absolutely. It's almost like building a business case for it as well.
You mentioned downtime. Was that part of the conversation? A focus area in terms of what can be achieved through chain shifting sort of the maintenance strategy?
James Bond: Well, reducing downtime is pretty much everybody's mindset. Nobody wants any downtime. That's going to cost them tons and tons of dollars or whatever, but that's what we're trying to avoid.
I'm trying to save this company money, but the main way we can see them doing it is reducing labor hours by using these predictive technologies and also reducing the downtime. Well, when you're reducing the downtime, you're also reducing the wrench time.
Niall Sullivan, Senseye: Oh absolutely. So I'm just thinking, in sitting in the shoes of someone or a company who currently employs a corrective maintenance strategy. So again, what would you advise on how to sort of shifting away from that? Maybe not straight to predictive maintenance, but sort of the steps towards that with some of the other maintenance approaches?
James Bond: I kind of have been thinking about a lot of this lately. The steps that personally I would do.
Let say, we're going to introduce a predictive technology tool. Determine what do you wanting to do with it and what do you want to use it on. And then once you've done that, hey, let's acquire it.
But in order to do that, might as well get some training because if you don't, you got a big expensive paperweight sitting on your desk.
And then after that collect as much info on the asset or assets as possible as you wish to test, but don't overwhelm yourself because if you overwhelm yourself by trying to start out too big, you'll be biting off more than you can chew. So start out small. Collect all the info that you can about your asset and the more info you collect, the better your results will be as far as your analysis goes.
Also, collect good baseline data for comparison. There always has to be a baseline somewhere so you know what your normal running characteristics are of the machine. But be consistent. In my field, and I am a strong believer in it, repeatability is the key. Being able to collect that sample or collect that data the same way, under the same running condition, each and every time, that way the only thing that really changes in your trend is your machine itself causing that. If you're not consistent with it, then your results will be all over the place.
Niall Sullivan, Senseye: Yeah. And that's interesting you mention data there, because it's such an important part of when you get predictive maintenance. Over the years, have you seen examples of data being collected the right way? Because a lot of it's down to context, isn't it. You could have lots and lots of data but if there isn't enough context there, it's difficult to get any insights. Have you experienced good data collection and bad data collection? What's your experience in that area?
James Bond: Yes. There are just certain things that we look for with good data collection and bad data collection. I had run across a technician one time, he was so stressed on trying to make a timeline or something that he had collected data off the same machine through a whole route and just collected it off the same machine.
But anybody with experience and know how, somebody like me, was able to pick something out. Then we just kind of have to help them work through all of that by trying to show them the importance of what that is.
Not so much scolding them, but 'Hey, we need to do it this way and this is the reason why we need to do it'. And the ultimate goal is to 'Hey, let's keep these machines running and being able to detect stuff, but we can't do it by having bad habits like that'.
Niall Sullivan, Senseye: And what types of systems do you use to collect data or to process the data from assets?
James Bond: Okay. Well we do have data collectors in which we're using triaxial accelerometers. We're using ultrasound. We're using tomography. Or let me change that. I am using all of those.
I have to be careful. So right now, I am using vibration, ultrasound, tomography. I'm using oil analysis. So there's even motor testing and stuff like that we also use, or I use. But yeah, all of those are very important, especially like you were talking about earlier about using more than one tool to be able to determine a problem.
I have a whole toolbox of predictive technologies that I use and if one tool out of that toolbox is not going to do it I'll go grab another one to see if I can figure out a little bit more what's going on with a different technology. If I can't figure it out that way, I'll throw the whole dang toolbox at it until I get it figured out.
Improving PdM With Maintenance Data
Moving from a reactive to a Predictive Maintenance approach requires a change of mindset and structure within an organization. From the top down, the focus must shift to one of continuity and efficiency through the use of data and technology.
Technology is a core enabler of Predictive Maintenance, whether collecting, analyzing, transferring or responding to machine data to keep machinery running; as such, the role of the IT team has never been more critical.
Download our free white paper to learn:
- The role IT plays in enabling Predictive Maintenance.
- The importance of maintenance data.
- Best practices for data collection and transfer.