Q & A with a maintenance expert - Part Three - With James Bond

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 third and final episode of this series, James reveals how predictive maintenance has helped him to overcome the challenges he faces on a day-to-day basis, the common mistakes to avoid when using such a tool and the benefits he has seen first hand of working with remote based colleagues. I hope you enjoy it.

You can listen to episode one here and two here.

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  1. The perfect predictive maintenance team
  2. Remote working and maintenance
  3. Culture shift required for successful PdM
  4. Improving PdM With Maintenance Data


Niall Sullivan, Senseye: I'm certain of that, and how has predictive maintenance helped you overcome some of those challenges we talked about before? Especially, if we compare it on the other end of the scale to reactive maintenance practices.

James Bond: Well, it's that culture change, then plus it's trying to use all your tools in your toolbox. Because there are a lot of times that you can't use just one, but just throwing that toolbox at it kind of takes the edge off of it. We would like to think, yeah I'm the man of the house, I'm the leader here, and we're going to do it just with my technology, and that don't always play into factor. So, it's being able to use all of these tools we have in our toolbox at our disposal. I mean, it's phenomenal in how much it decreases the difficulty of what you're trying to accomplish.

Niall Sullivan, Senseye: Yeah, I certainly get your point there of what you're saying. So, not one method is a silver bullet, it's not going to solve everything. It's some of the themes I've already covered. But it's about layering different technologies, or firing different tools, as you would say, on top of it to achieve the outcomes that you've talked about. So, we also touched on a little bit in terms of staffing and predictive maintenance programs.

So, I guess a misconception from time to time is that you need a big team and a big function behind you in order to deliver predictive maintenance. Do you agree with that statement, or a lot of resources let's say, in order to get up and running with predictive maintenance?


James Bond: Well, I guess with any group you're going to... The more skilled technicians, your... Like I said back, those certifications make a big difference and that helps, but that maintenance experience also helps as well. The thing about it is that's a good thing that management can determine is okay, who is the most skilled at this position? And what are their strengths? What are their weaknesses? In order to be able to place them with the right technology? So yes, I would agree to a point that it takes a little bit of size in order to have a good team.

But not necessarily whenever there's say a smaller facility, it's just as long as you have the right people in the right place, you can make a few people go a long way.

Niall Sullivan, Senseye: And what would be the positions of those people, or titles, that you'd need as the core function of that team?

James Bond: Personally, I would look for the ones who does have the maintenance backgrounds. I've had to open the eyes on some technicians that never did really believe in what you were doing. It was just black magic or something like that, see who performs. No, I don't believe in that, but whenever you start showing them visual proof, "Hey, this is what we're doing. Look man, you worked through this and we produced results by doing so." It is just a matter of making the believer out of people, and then it takes those people...

I worked with a technician one time that worked on the maintenance side of things, and because he had that maintenance background was brought into a group that I had worked in. And then it had really opened his eyes on my perspective on things or my team's perspective on things. People like that, as long as you can make a believer of them, make them a strong point about it, they come from that maintenance background, that helps out a lot. Because even though you may know something about that machine, the next person down the line may not. So, just finding the people with a wide variety of skill sets, should I say?

Niall Sullivan, Senseye: Yeah, so they can all bring something unique and different, a different perspective to the table and different skills to the table.

James Bond: Yeah, a group that I work with right now, there is a wide variety of skill sets right now. And the team I work with right now is extremely knowledgeable and every one of us brings something very unique, which what builds a strong team. We're a very diverse group, too.


Niall Sullivan, Senseye: Fantastic, as it should be. I was going to ask, actually, in terms of that team makeup, is that a mix of remote staff, non-remote, or are people... Just out of interest, really?

James Bond: Yes.

Niall Sullivan, Senseye: Yeah, and does that have its challenges? I mean, even I work, as you can see, in a remote environment. But does that bring up any challenges in a factory floor maintenance environment or opportunities, actually, let's say as well?

James Bond: My perspective, I don't really believe so, because some of those skill sets may have somebody who can work better in an environment where they're not bothered and have to really focus on something that's right in front of them, versus, there's somebody that needs to go into work and actually lay their hands on something or actually collect the data off something to be able to do this analysis. So, it just depends upon the skillset.

Niall Sullivan, Senseye: Yeah, that absolutely makes sense. I was just going to look at it from a different perspective, we'll go onto predictive maintenance. So, what are some of the common mistakes to avoid when using the predictive maintenance tool? I guess, one comment you made there about it being black magic is probably one, but it's not just a connect it up and then there you go, job done, kind of thing.

James Bond: Right.

Niall Sullivan, Senseye: If only it was that easy, it'd be great, wouldn't it? But are there any other common mistakes that people make or assumptions people make or misconceptions?

James Bond: Okay, main conception, and I have fun with this, they want to know when it's going to fail, the exact time. There's really no possible way of doing that, even though they would like to think so. It could fail tomorrow or it could fail next week or next year. Currently what I tell everyone, "I'm sorry, but our crystal ball is being calibrated at this time, so there's no way I can determine that."

Niall Sullivan, Senseye: That's a good way to respond. Yeah, they might be good, but they're not quite at that stage where a tool can actually pinpoint the exact moment a machine's going to fail. I mean, that'd really make your job easier, wouldn't it?

James Bond: Yes.

Niall Sullivan, Senseye: Instead of the planning, it'd be... That'd be the dream, really.

James Bond: Okay, another one that I have really seen that's most common, people like to start out too big. They want to pull a big group of people in, instead of building their case, they want to get in here and before you know it, you're biting off more than you can chew. You have to take your time, like you've heard before, Rome wasn't built in a day. So, take your time, work with small stuff and build off of that. Another thing, being consistent... We've talked about it earlier, being consistent with your data or sample collection.

If you're not doing that, then you're not practicing that repeatability and you're not going to have sustainable results. And another one that I'm a big firm believer on it, and people I have worked with before have noticed this out of me, practice safe work habits. Those machines don't care who you are and they will eat you alive. They don't care if they rip that finger off or anything, their main purpose is to... I'm going to run until I break, or until I'm shut down. So, you have to be extremely careful around your surroundings, your environment, stuff like that.

Niall Sullivan, Senseye: Is that something else that forms part of a business case about improved health and safety? Because like you said, a better maintained machine is less likely to fail and cause potential injury to someone on site. So, is that something that's into the consideration? And with downtime being maybe the main consideration, but just out of interest, really? Because I know there's other industries that focus on health and safety as quite a big factor.

James Bond:  Safety is my number one priority. I can go up there and I can look at stuff, and then if I can deem that to be unsafe... Think outside the box. Okay, if I cannot do it this way, let's figure out a way where we can look at this where we're out of harm's way.

Niall Sullivan, Senseye: That's a really good way to look at it, really. So, I wanted to look ahead, we've looked a bit into the past, about what maybe maintenance looked like before. But I wanted to look into your crystal ball, James, as you said earlier. Do you have any thoughts about how maintenance is going to evolve in the... I won't give a time frame, but just in the future in general, where is it heading?

James Bond: A lot of things I do right now is condition-based monitoring, I do online monitoring, and I see that moving on forward basically using artificial intelligence. It's amazing on some of the artificial intelligence programs that are out there. Of course, you know one of them that I'm real familiar with, and it's amazing that everything that I look at on that, it's another learning curve. And after doing it for so many years, that you kind of pinpoint, you see something, but there's always going to be something out there that... Okay, now what can we do, or what can we measure now to get this result out of it in order... And then work from there?

Niall Sullivan, Senseye: Yeah, I guess you're constantly learning as you have over the past... It's that kind of thing, there'll always be something new. So, I guess it's about not being a dinosaur, as you put it, and maybe it's being open-minded about these new technologies, and maybe not believing their magic, as such, but at least being open-minded to the possibility that there's some tools out there that can really make your day to day life easier.

James Bond: I'm not a happy person whenever I cannot go into my position every day, going to work every day, and not be able to learn something new. And that's what it's going to take to make stuff like condition-based monitoring, predictive maintenance of stuff to continue to grow, because you're continuing to think outside that box. Okay, what can we do now? What are we missing here? What can we bring to the table that we're not currently doing right now? And it's that constant stride, that constant yearn for knowledge.


Niall Sullivan, Senseye:  And do your maybe not necessarily colleagues, but even peers, do you feel that they're really on board with these type of technologies or is there still a level of culture shift? I know it depends by organization as well, but it just on a maintainers level, is there still a culture shift that needs to happen there, or do you think people are moving towards this?

James Bond: I'd say with the proper knowledge, through the proper training, it takes training to make believers out of people, then plus the experience. Okay, you've got all this knowledge, you've been trained, now let's send you out in the field a little bit. Now, we're going to get firsthand experience on what you have been learning, so go out here and it just... That's where you're opening the eyes and the clouds have parted, the sun comes out and it's an aha moment, and that's what the real neat thing is about it. I go into my facility every day and I just... Hopefully that I can just go in there and I see something, and it is something that I normally don't see. And then I go to this machine, I go, "Here we go," and it's being able to take what you have learned and everything, and making the believer out of you, into being something productive.

Niall Sullivan, Senseye: That's really nice way to put it. I've got a couple of questions left, and this one might seem a strange one, but it's about how do you achieve greatness as a maintenance professional? It's maybe a bit more of a fun question, I guess, to end things on.

James Bond: Here we go, I got a good one for you on this one.

Be open, be consistent, think outside of the box, work as a team, that's a biggie for me, and have lots and lots of patience. You have to have patience in order to accomplish what you're trying to achieve.

Niall Sullivan, Senseye: And is that what you'd... I guess maybe the same question packaged up differently, but is that what you'd advise your younger self from a maintenance point of view, as well?

James Bond: Yes, whenever I was younger, I had very little patience with anything. Now, I would like to think I have lots and lots of patience, even though I do have a way of pushing buttons every once in a while.

Niall Sullivan, Senseye: Well, we're all human aren't we, in fairness? So, we all have our limits, don't we? But no, absolutely, I think that's really great advice. And then finally, just to finish on this, is it, especially when you talk about AI machine learning, someone maybe outside the industry just hears about, robots and stuff taking over humans in facilities. Do you have a view? Are you concerned that technology might one day overtake humans or take away the human input in your type of role?

James Bond: No, something's always going to short circuit. Really, just by reducing the need for human input in one area, that only gives us an opening and helps us utilize our time in other areas that are not as strong, and being able to work on that area and strengthen it. And then by doing that, you're also widening your skillset, you're learning something new, you're learning something different, and you're making yourself and then plus your team a valuable asset.

Niall Sullivan, Senseye: Excellent. So yeah, so I think that's all we've got time for today, but it's been really great to talk to you, James, and learn a bit more about maintenance and the history behind that, and what you're doing today, more importantly, as well. So, thank you for taking the time out.

James Bond: Yeah, like I said before and I've told several people, I have several grandchildren. I have a large family, and say my grandson would want to go into the facility at work which I work at, I want to be able to leave that place knowing I did something to improve it, and possibly make it a little easier on him in the future, whenever he gets ready to decide what he wants to do. And if that was the case, I've done something to help the future of my family or my children or my grandchildren.

Niall Sullivan, Senseye: Yeah. What a lovely sentiment to end on, as well, fantastic. So yeah, thanks again, James, it's been a real pleasure.

James Bond: Thank you.


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.

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