Accelerate Innovation Engineering | Podcast 1: Condition Monitoring and Predictive Maintenance| Podcast 1: Condition Monitoring and Predictive Maintenance
In our new 3-part podcast series, “Accelerate Innovation Engineering,” experts from CBT and HPE will talk about how industrial IoT contributes to building a successful predictive maintenance strategy and more. In episode 1, Preston Johnson, Sr. Solution Manager for Asset Integrity & Reliability presents on Condition Monitoring & Predictive Maintenance with host, James Hilliard.
Well hello there everyone, James Hilliard here. Welcome to our podcasts. This is part of a series of conversations with the team at CBT. Their focus is dedicated to bridging the gap between Informational and Operational Technology. Today we are discussing condition monitoring and predictive maintenance. My guest on board here is Preston Johnson, Senior Solutions Manager for IT/OT industrial operations and maintenance at CBT. So here’s what we’ve got on the docket for about 10 or 15 minutes here today, we’re going to look at a holistic view of condition monitoring and predictive maintenance, why it’s important, and touch on a little bit of ROI. We’ll talk about developing this solid strategy, so you can support open architecture systems today and also tomorrow because that’s important. And then we’re going to touch on this concept of digital twins, the virtual representation of physical assets or processes. We’ll talk about that how that plays into this whole condition monitoring and predictive maintenance space. So, Preston, that’s what we’re going to be covering here for the next 10 or 15 minutes. I’m glad to have you on board.
Well, thank you, James. It’s a pleasure to be here. Now, these are really my favorite topics. And I’ve been working in this space for well over two years, from developing condition monitoring and predictive maintenance products to predictive maintenance strategy, consulting, and more recently deploying open and holistic systems for condition monitoring and predictive maintenance.
Well, let’s get into the idea here of why condition monitoring, why predictive maintenance, and why have these tools become so important to teams these days?
Well James, in asset-intensive industries, such as chemicals, oil and gas, power generation, mining, food and beverage, and automotive, our physical assets, like our pumps, turbans, heat exchangers, generators, spans, kilns, and mining, performs the work that produces our products. The performance of those assets is paramount to production output, production quality, production costs, and even to the safety of our workers. So really, if the equipment performs well, we get a lot of product. From a return on investment perspective, our well-performing equipment is reliable, and you can count on it to produce your products. Those well-performing assets or equipment produce actually higher quality products at lower energy costs and operate more safely. With condition monitoring and predictive maintenance, maintenance is less expensive and less than interruptive to production. All said revenue increases as equipment produces more products. Costs such as energy and maintenance and labor are reduced. With reliable equipment, environmental costs are reduced because we use less energy and we waste less material. Safety increases as the equipment are more reliable and predictable in its operation. And what’s really cool is all this can be measured and compared to industry best practices. For example, in best practice cases, for good condition monitoring and predictive maintenance, we usually get two to three months of lead time to plan and schedule all of our maintenance activities, which saves us expedite costs over time, labor, and surprise shutdowns. In best practice, 90% of the work that we do to maintain and operate our equipment is scheduled after it’s been planned at least three weeks ahead of time. And only 10% of the work that we do, or even less better, yet less sneaks up on us and causes that unscheduled downtime. So really, the return on investment of condition monitoring and predictive maintenance is improving revenue. Because we produce more, it lowers our maintenance and our operational costs, we use less energy. It’s less expensive to maintain our equipment, and fundamentally increases the safety of our plant.
Every time I think about technology, I also think about the people aspect. If there’s some surprise failure of something, people are then in scramble mode. And sometimes when we’re in scramble mode, we don’t make the best decisions. We could make a mistake, or we could miss a step. But the idea of knowing that if this turbine looks like it’s on its last legs, we’ve got some bearing issues or what have you. So let’s schedule the time and all that you can just be so much more focused on what you’re doing. You’re laid out again, all the return on investment of having something here. So let me ask you this in terms of industrial IoT, it’s all these systems and monitors that we have to help us understand and gain that insight? How does the industrial IoT really help contribute to building that predictive maintenance strategy side of it and let teams really have a strategy, not just a reactionary, something broke, got to fix it.
That’s a great question, James. From a holistic view, we need to see a big picture of our equipment, not just the existing sensors that kind of come with the equipment. The operators and programmable logic controllers typically control the flow, process, pressure, temperature, speed, etc. Those sensors can give us a good idea of the performance of the assets and equipment, but they’re really not good predictors of when we need to perform maintenance. So we need more of a holistic view of the equipment, when we need our maintenance, and address the wear and tear or the degradation of the equipment. If we don’t address the wear and tear, or degradation, then performance drops, and ultimately production stops. So what we need to do is to be able to somehow detect and track that wear and tear so that we can understand the functional health of our equipment, and plan mitigation, any unplanned stoppages. And there’s where we start to add in the industrial IoT sensors, like vibration and motor current and oil analysis, ultrasound, and even thermal imaging. Each of those sensors detects some but perhaps not all of the wear and tear defects that will likely cause our equipment to fail. Those IoT sensors catch the degradation of the equipment and the patterns early on so that we have the opportunity to plan and schedule the maintenance well ahead of the failure. Typically months before the problem is really likely to cause us an unplanned outage. For example, vibration can detect defects in rotating equipment, bearings, gearboxes, cavitation, pumps, unbalanced fans, and many more defects. On the other hand, oil analysis can help us detect viscosity breakdown water in the oil and other contaminants in the lifeblood of the machine. And motor current analysis can help us detect problems with the motor like rotor bars and windings and the eccentricity of the air gap in the motor. So all of those together give us that holistic picture. And if we design and look at our IoT system sensors from an open architecture perspective, that gives us the ability to mix and match across the vendors to use best in class technology to use what we already have in place. So they don’t have to replace it and be able to expand and grow over time.
One of the things I mentioned was this idea of digital twins, which is being able to represent physical assets or processes in a virtual manner. How is that playing into this condition monitoring and predictive maintenance space? Finally, we have this technology that we can use to advance ourselves.
Historically, in the condition monitoring space, the number one thing to do was use vibration to detect defects in equipment, and then use that vibration and of itself to tell us what kind of maintenance. And that was sort of a silo view of the equipment. What digital twins give us the ability to do is bring all of the holistic information together and help us manage our assets over their entire lifecycle. Digital twins contain the performance specifications and how much flows should we expect from this pump. Digital twins include the likely degradation patterns and the failure modes of the equipment we might expect to see. They tell us what sensors we could use to detect those degradations and what the thresholds might be for that equipment. Digital twins help us keep track of the preventive and predictive maintenance that we perform, such as records and costs, work procedures, work plans, and so forth. They keep track of the trend data from the control system and from all those industrial IoT sensors that we might add. So we can see how things are ebbing and flowing in our overall process. They help us even contain three-dimensional models and process and instrumentation diagrams so we can actually visualize and understand how to take apart a machine and how to put it back together. Digital twins really bring our predictive maintenance to life. And going further, when we connect these digital twins to our workers in the field, we call them connected workers. That allows us to enable augmented work instructions better digital workflow so that the workers in the field are connected, and they see all this information as they’re performing their tasks. Some digital twins are the accumulation of all of the data engineering specifications, procedures, three-dimensional drawings, and so forth, that give us that holistic view that allow us to be most predictive, and plan ahead with our maintenance and our operations activities.
I appreciate it, Preston. If you want to reach out to the CBT team, you can visit their website at cbtechinc.com. There are a couple of great case studies to read up on as well as other archived webinars and other podcasts in the series. You can also email them at email@example.com. Thank you to Preston and the audience for joining us. James Hillier and the entire CBT team look forward to talking to you all down the road.
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