A maintenance technician walks the plant floor of a pet snack manufacturing operation as nearby machines whine and rumble. He stops in front of one machine, donning his augmented reality (AR) glasses to view diagnostics for the machine, to find that a bearing on the equipment will fail soon, according to vibrational analysis. On his glasses appear the storage location of a replacement part as well as instructions for how to complete the needed repair.
This is where plant maintenance is headed sometime in the next 10 years, expects Joe Zembas, plant engineering manager at food and beverage manufacturer J.M. Smucker. “You are given the information to properly shutdown, lock out, and replace the part like a video you’re an active part in. You return the machine to operation,” he says. “You’re capable of doing this without years of training as a mechanic.”
The scenario today is quite different, yet the technologies needed to make this future plant maintenance a reality are already in place or being actively rolled out. The list includes Internet of Things (IoT) sensors, analytics based on machine learning, digital twinning for simulations and evaluations, and AR and virtual reality (VR) for training and instructional how-to.
But even deciding where to start this technology transformation at an older plant can be difficult. And older equipment might not be able to support this new form of plant maintenance. Cost is also always a consideration.
Preventive to predictive
Today, it’s typical for manufacturers to practice preventive maintenance, according to Hans Van der Aa, president of SupportPro, the aftermarket service arm for all Duravant equipment. Preventive maintenance replaces parts and components on a regular schedule, based either on the calendar or usage. It’s a step up from a reactive approach, where maintenance happens after machines fail. Preventive maintenance eliminates roughly 80% of all unplanned downtime, according to Van der Aa.
However, a preventive approach means that manufacturers are swapping out parts while they still have some operating life left in them, thereby driving up component cost. It also causes a drop in production throughput because machines must go offline for maintenance at what might not be the optimal times.
The next step up from preventive maintenance is predictive maintenance—something that only larger companies are doing today, Van der Aa says. An example of preventive maintenance would be to monitor a motor for vibration or current draw. A change in normal operating conditions could indicate that a bearing is wearing out or about to fail in some other way. With this detected well in advance of potential problems, the plant could schedule maintenance for a time with the least impact on operations while getting the most out of the equipment.
“That is what everybody is looking for,” Van der Aa says. “It can be done, but it’s not as straightforward as some people had hoped for because in the end you need to know the equipment well enough.”
IoT sensors that collect data on vibration, temperature, heat, current draw, and more can provide the raw information needed for such an understanding of plant machinery. Early vibrational analysis consisted of a technician walking around on a schedule, collecting data from a few selected spots on some machines, recalls Smucker’s Zembas. Today, mounted IoT sensors can continuously monitor parameters like temperature and vibration. If connected to a plant network, those sensors can provide a constant reading on machine health.
A recent report from Market Data Forecast predicts that the global industrial IoT market will grow from $300 billion in 2020 to $895 billion in 2026. Analysts project manufacturing will expand the fastest at a 24.3% compound annual growth rate.
Falling prices and increasing performance of IoT technology are driving that growth, as well as a desire for data. Real-time monitoring can help boost overall equipment effectiveness. As a result, condition monitoring programs are becoming more common, notes John Davis, a business development associate and solutions engineer for system integrator Quantum Solutions.
“Predictive maintenance strategies can lengthen a machine’s lifespan by addressing issues before they develop into expensive failures, while reducing unnecessary maintenance,” he says. “They can increase reliability and reduce costs.”
Analysis and simulation
An ability to detect patterns in this potentially huge amount of sensor data is critical. For example, data trends can show jagged peaks and valleys simply because of changing loads on the machine. A batch mixer, for instance, might see vibration spikes during the beginning or end of a run. Because of this, an engineer might set relatively large out-of-spec margins—which in turn could conflict with the ability to spot signs of trouble as early as possible.
Traditionally, the type of knowledge necessary to detect such signs resided with a maintenance technician—someone who might have been on the job for decades, comments Jonathan Darling, consumer packaged goods industrial automation market segment leader at Schneider Electric. That experience makes it possible for a tech to walk through a plant, listening to the sounds of the machinery or otherwise collecting information on operating conditions. Like someone driving a familiar car, the tech would be able to tell from the sound and other clues when something is off and then take corrective action.
One issue with this method is that it takes time to acquire that experience. Another problem is that it can lead to a conservative approach—a tendency to swap parts out early and continue to do things the way they’ve always been done.
Machine learning offers an alternative. With this technology, software ingests large amounts of sensor data from both good and bad operation, which can be defined as making in- or out-of-spec product or by some other criteria. From this information, software builds models used during day-to-day manufacturing to find problems. In manufacturing and elsewhere, machine learning has proven very successful in finding these hidden clues.
Machine learning, though, requires data—the more the better. The data must be from times when the machinery is working as desired and also at times when it is not. In a manufacturing plant that is performing well, it might be hard to come up with out-of-spec examples, making it take longer to collect the needed data.
The money must be spent upfront to put in all the sensors necessary to gather the data. “There’s a whole conversation around cost, with investing into adding those sensors or monitors to those pieces of equipment in the facility,” Darling notes. “But what is the cost of a piece of equipment going down?” The stoppage figure for a large CPG operation can be in the tens of thousands of dollars per hour, he points out.
Sometimes there are follow-on indirect costs as well. A chicken processing line, for example, might have only a limited amount of time that product can be left hanging on hooks because of health concerns or regulatory requirements, Van der Aa comments. A machinery breakdown that lasts long enough can lead to waste with raw product being scrapped and discarded.
Besides machine learning, another technology that can make use of IoT sensor data is a digital twin. This is a software replica of a physical system that responds to input changes in ways that mimic what happens in the real world. A digital twin makes it possible to get an idea of what will happen without it actually having to happen. Maintenance staff can simulate what variations in input feed rate, for example, would do to a machine’s output.
This capability makes it easier to troubleshoot problems, potential or otherwise, and investigate solutions without taking systems out of production. It also allows experimentation, such as what will occur if technicians adjust a system’s operational parameters. Being able to see such effects could lead to a benefit beyond maintenance.
Someone who has decades of experience might be skilled at keeping equipment up and running, Darling notes, but that might not be the optimal operating point. A digital twin offers a way to find the operating conditions that lead to the best possible outcomes.
Overcoming a skills shortage
A final area where technology advances promise to change maintenance for the better is AR and VR. Both present images to a user’s eyes, with these being either completely digital in origin in the case of VR or digital information overlaid on a real-world image for AR. Oculus, Microsoft, Meta, Apple, Sony, and others either have or will likely soon have products in this space. Most of these are aimed at consumers, but some are already being used in industrial settings.
These developments are attracting the attention of machine makers. “At Gericke, we’ve been paying attention to innovation in augmented and virtual reality where all the equipment documentation and maintenance steps are in front of the operator visually at any time, for example,” says Rene Medina, executive vice president at Gericke USA.
One reason to roll out this technology is the growing shortage of skilled maintenance personnel as well as the retirement of experienced workers. Training replacements and getting them in-the-field experience takes time. AR could be used to fill in the voids in the current landscape by providing distilled information and guidance, Smucker’s Zembas says. “It’s an ability to address both the skills gap as well as the standard maintenance activities.”
AR technology need not involve glasses or goggles, which today can struggle in brightly lit settings and other environments. The display could be something as simple as using a tablet or phone to scan a QR code on equipment, thereby pulling up information on a machine.
VR can be particularly useful in training scenarios. It can allow trainees to practice a repair task on a simulation, for example, giving them valuable experience without having to take an actual machine out of service.
“We need to bring down the learning curve by making it easier to understand systems and what is going on,” Darling says.
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Though IoT, analytics, digital twinning, AR/VR, and other technologies—even using 3D printing to overcome spare part supply chain issues—are promising when it comes to maintenance, there are still challenges to overcome. One is a perceived risk of change, Darling notes.
In greenfield plants, there is plenty of willingness to embrace the latest technologies. But legacy operations are a different story. Some food and beverage manufacturers have 20-year or even older control systems. And if the machines are running and producing goods at a profit, it could be difficult to overcome a reluctance to deploy new technology. It might even be the case that implementation requires ripping out the old, yet working, system entirely.
Paralysis in rolling out these innovative technologies can also arise when users are confronted with many different places to insert them. One approach to overcoming this hurdle is to identify which assets are critical and begin by implementing maintenance changes there, advises Quantum Solutions’ Davis.
In general, hardware is not an issue, Van der Aa contends. Sensors and other technologies are at the point that generating and collecting data is not a problem. Analysis is more difficult because it involves turning that data into actionable information. A typical first step, he says, might be to produce a dashboard with gauges to indicate plant health. Dashboards, though, do not say what to do once a problem happens or is predicted to happen.
Counterintuitively, the many technology options and choices can make achieving this goal of actionable feedback difficult, and so simplification can be helpful. For a maintenance service company like SupportPro, there is also the fact that customers are conditioned by office software, phones, and computers to expect continuous evolution and improvement. An answer for needs involves picking the right products and developing easier solutions, an approach SupportPro is exploring.
“At our company, we’re working on a standardized platform so we can make it a lot easier on our customers,” Van der Aa says.
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