What Are Predictive Maintenance & Conditions-Based Maintenance? (Interview with Dr. Klaus Blache)

Predictive and Conditions-Based Maintenance Image

It’s always great to talk with someone who really knows their stuff, and when it comes to issues around maintenance, reliability, and maintainability, Dr. Klaus Blache of the UT-RMC really knows his stuff.

That’s why we’ve enjoyed holding (and publishing here) a series of discussions with Klaus on issues related to reliability and maintainbility (if you missed some, check out our recorded discussion on the benefits of reliability and maintainability and articles we co-wrote on creating a culture for reliability, maintainability, and continuous improvement as well as what reliability and maintainabilty are).

So with no further ado, please feel free to watch our short discussion explaining predictive maintenance and conditions-based maintenance (also known as PDM and CBM) below. If you’d rather read instead of watch and listen, we’ve created a transcript for you below the video.

And before you go, please feel free to download our free PDCA Cycle infographic from the bottom of this article.

An Introduction to Predictive Maintenance & Conditions-Based Maintenance

Below is a (pretty close to word-for-word) transcript of our discussion with Dr. Klaus Blache about Predictive Maintenance (PDM) and Conditions-Based Maintenance (CBM).

Convergence Training:

Hi, everybody and welcome. This is Jeff Dalto with Convergence Training, a Vector Solutions brand. And we’re back with a little recurrent discussion on issues related to reliability, maintainability, and maintenance with Dr. Klaus Blake. He’s the director of the Reliability and Maintainability Center at University of Tennessee, also known as the UT-RMC. Klaus, how are you today?

Dr. Klaus Blache:

Doing great, Jeff.

What Are Predictive Maintenance & Conditions-Based Maintenance?

Convergence Training:

Cool. Thanks a lot for joining us. And today we’re going to discuss predictive maintenance and condition-based monitoring. And I wonder if we could just have you explain what the each of them are and what’s the difference between the two of them?

Dr. Klaus Blache:

Well, I thought I’d start out and maybe kind of read the two sentences that are the predictive maintenance definition from SMRP to look at and then I’ll say a few words beyond that, to tie those together.

Now, a kind of a formal definition from SMRP is predictive maintenance, or often PDM, is an equipment maintenance strategy based on assessing the condition of an asset to determine the likelihood of failure. The key is the likelihood, it’s not an exact time, like the failure and then taking appropriate action to avoid failure. The condition of equipment can be measured using condition-monitoring technologies, statistical process control equipment, performance indicators, or through the use of human senses.

Now again, the key thing here is note that PDM or predictive maintenance only indicates the likelihood of failure. It kind of puts you in a window of time that you can go do something about it.

Now conditions-based maintenance, or some say conditions-based monitoring, is performed only when it’s required, meaning that you’re doing the conditions-based monitoring all the time, but it triggers only when you actually go do the maintenance. So then it’s usually in response to a deviation from a normal condition of the machines.

So conditions-based maintenance, or CBM, uses real-time data, typically, and looks at trends regarding the asset’s health. And that indicates one to take action by comparing historical data to current data. And then it watches predetermined limits, and when those are passed, that is your opportunity to do that maintenance, you kind of do it when you need to do it, and then not before that.

So in summary, using CBM or conditions-based maintenance, it should only be performed when specific indicator show signs of decreasing performance that can lead to failure. So one, CBM, is a lot more specific. And the other one, PDM, kind of gives you a window of time.


Examples of Predictive Maintenance (PDM) and Conditions-Based Maintenance (CDM)

Convergence Training:

All right, great, thanks, that makes sense, good explanation.

Could you give us an example of each, one example of predictive maintenance and one example of conditions-based maintenance or monitoring?

Dr. Klaus Blache:

Sure.

They work with some of the same technologies. So if you look in the gamut of predictive maintenance, and examples of predictive maintenance, pretty much all the well-known technologies that are always talked about, like ultrasound, vibrational analysis, infrared technology, motor circuit analysis, and I’ll throw in oil analysis. Now condition-based maintenance uses sensors, and also all these same technologies, as needed to trigger maintenance with enough lead time to avoid the failure again. Now, the difference is that measurement is typically continuous, and can be done while the equipment’s running. So that’s a big differentiator, versus operators using the technologies on PM routes.

Convergence Training:

Gotcha.

Dr. Klaus Blache:

CBM uses strategically placed sensors to monitor things like temperature, pressure, flow, and so on, in addition to doing the predictive technologies, and preferably, again, it’s done real time. So you can get that stuff as quickly as possible.

So you can do conditions-based maintenance with just PM technology routes, but when you don’t do it continuously, you’re obviously increasing that window of opportunity that it’s going to be less effective in detecting the problem, you know. So if you don’t do the monitoring continuously, you might do too much maintenance or not enough maintenance, just working within a window, doing technology routes.

When to Use PDM or CDM

Convergence Training:

All right, great. And so what would be your recommendation for when to use predictive and when to use conditions-based monitoring or maintenance?

Dr. Klaus Blache:

Well, the answer really comes down to which approach is more cost effective.

I stated that earlier conditions-based monitoring is performed when the asset’s running. So production’s not disrupted. So that’s a big benefit. But at the same time, I CBM also avoids doing too many maintenance interventions, which can cause other issues due to human error, poor maintenance practices, and more. So CBM obviously is going to be a little bit more expensive, and requires a little more analytical skill set. But sensor costs have come down along with increasing wireless capabilities and computing powers, there at a lower cost. So a lot of things are right for taking more advantage of CBM more recently.

And I always like to say the best solution is the simplest one that works. So to set up your program, you know, put together a matrix of assets versus predictive technologies, and then take a look at what can be done, what should be done, and what do you have resources to do, so you can get the biggest bang for the buck. Then select the failure modes that can be detected by the conditions-based monitoring or maintenance, and then align them with feasible technologies and decide what’s economical. You know, maybe PM or technology routes are worth it because of the risk involved or less risk involved. But in certain percentages, you want to do complete conditions-based monitoring real time all the time. It’s a cost-effective solution in the end.

Convergence Training:

Including apparently using risk management at times for making that decision?

Dr. Klaus Blache:

Absolutely.

Declining Costs for Maintenance Sensors

Convergence Training:

Okay, great. You mentioned that some of these sensors have gone down in price Do you anticipate that will continue?

Dr. Klaus Blache:

Oh, I think absolutely. And especially with a lot of the new stuff. The latest buzzword…I was one of the keynotes at the OSI Soft conference in April in San Francisco. And you know, probably half the vendors there were either displaying or talking edge computing. So that’s really bringing all the computing power down at the local level. And that’s going to enhance it even more.


Tips on Maintenance Metrics

Convergence Training:

Gotcha, cool. And any metrics or things people should be aware of, to see how they’re doing in this area?

Dr. Klaus Blache:

There are a lot of things that could be done. And maybe I’ll just mention a few cautions, let’s say if you’re starting out in PDM, or hopefully conditions-based maintenance, I’d suggest separating your technology compliance from your total maintenance compliance. And so I’ll just make up an example, if you have 1000 maintenance tasks that need to get done that month. Typically, 100 of those might be predictive-technology related, and 900 everything else. So you can still be 90% compliant and not do any of your technologies. And that’s something, unfortunately, I see in a lot of companies that are struggling, they’re not doing their predictive technologies.

And from surveys of companies, mine and others, about 60 to 70% of companies are using the key technologies that were mentioned earlier–all the predictive technologies–but they’re not being used on enough assets. So again, they’re resource constrained, they might be using the technologies, but maybe on only 20% of the assets, the critical assets.

And there’s insufficient root-cause analysis once they find the problem. So they don’t take it to the next step. And whether that’s resource or skills, that’s probably not the right discussion for this.

Also, about 60% of companies don’t feel that the predictive program is working well. You know, that’s a big one. And my data shows that over 75% of North Americans are not doing enough finding and fixing, but finding and using your predictive technologies, finding an issue, but then also driving it to root cause. And then putting out the work orders to do that. And 75% of North America is not there. And that’s huge.

And if you’re highly reactive, let’s say over 60% of which gets you to about the bottom quartile, your maintenance costs are six to seven times higher than the average top-quartile facility in North America. So if you’re not doing that finding and fixing, if you look at a long enough period of time, you’re just fixing those same errors over and over again. You’re at Einstein’s definition, you know, doing the same thing over and over again and expecting different results, hat’s insanity. I like to say doing the same maintenance over and over again, over a long period of time, that’s maintenance insanity. That’s what 75% of North America is doing right now.

Convergence Training:

All right, great.

The Purpose of Reliability & Maintainability

Dr. Klaus Blache:

Maybe a maybe a closing comment, you know, it’s probably important to keep in mind that the job of reliability and maintainability is not just to fix things when they fail. The goal is to maintain the assets in such a way that the failure is engineered out, avoided, or the consequences of failure are avoided, or simply stated, reliability and maintainability needs to be designed in. So it’s a designed-in function if it’s done right.

The UT-RMC’s Reliability & Maintainability Implementation Certification

Convergence Training:

Right. And we talked about that in our previous discussion. Well, if I could add one opportunity for a final word, there’s a lot to learn here for people. Could you tell us a little bit about the UT-RMC and your RMI certification?

Dr. Klaus Blache:

The RMIC is a reliability and maintainability implementation certification. It’s basically six professional development classes, three can come from our training partners, RedVector being one of those. So you can take 120 hours of RedVector 40 hours per class of online, that counts as three classes, and then three classes come from us. You do a project at the end, not a white paper, but a measurable deliverable with real results back to your facility. And that certifies you for the RMIC certification from UT College of Engineering.

Convergence Training:

All right, great. Well, thanks again, Klaus. Maybe we’ll do this again next quarter if you’re free. I’ve already got some ideas off this discussion, we appreciate your time, and tell Chris we said hi.

Dr. Klaus Blache:

Will do, thanks Jeff.

Conclusion: It’s Important to Know What Predictive Maintenance & Conditions-Based Maintenance Are (And When to Use Them)

We hope this discussion with Klaus helped firm up your understanding of predictive maintenance and conditions-based maintenance (as well as reliability and maintainability). Stay tuned for another discussion with Klaus soon and feel free to check out some of our online maintenance training courses, including mechanical maintenance, electrical maintenance, asset condition management, total productive maintenance, and more.

And hey, before you cut out, why not download the free PDCA Cycle infographic below. 

 

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FREE PDCA Cycle Infographic

Download this free infographic of the Plan-Do-Check-Act (PDCA) cycle commonly used for quality control, project planning, and continuous improvement.

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Jeffrey Dalto

Jeffrey Dalto

Jeffrey Dalto is an Instructional Designer and the Senior Learning & Development Specialist at Convergence Training. He's worked in training/learning & development for 25 years, in safety and safety training for more than 10, is an OSHA Authorized Outreach Trainer for General Industry OSHA 10 and 30, has completed a General Industry Safety and Health Specialist Certificate from the University of Washington/Pacific Northwest OSHA Education Center and an Instructional Design certification from the Association of Talent Development (ATD), and is a member of the committee creating the upcoming ANSI/ASSP Z490.2 national standard on online environmental, health, and safety training. Jeff frequently writes for magazines related to safety, safety training, and training and frequently speaks at conferences on the same issues, including the Washington Governor's Safety and Health Conference, the Oregon Governor's Occupational Safety and Health Conference, the Wisconsin Safety Conference, the MSHA Training Resources Applied to Mining (TRAM) Conference, and others.

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