Predictive maintenance can grow far beyond traditional condition monitoring when the data from equipment is gathered through the Industrial Internet of Things (IIoT) and then stored and processed through Big Data analysis systems, such as IBM’s Watson. “When you gather the regular maintenance data, you can build a history of the data. Then you use algorithms to detect anomalous behavior in the historical data. In time, you learn that when you see this anomaly, you know—based on the history—that this component is likely to fail in the next 10 to 17 days,” Tom Craven, VP of product strategy at RRAMAC Connected Systems, told Design News. “The analysis of the data can predict the very specific failures in a specific timeframe. That’s where IBM Watson comes in.”
|Sensors can gather equipment condition data and send it via the IIoT to a service company that can use it for predictive maintenance. (Image source: RRAMAC)|
Craven will present a session at the Atlantic Design and Manufacturing Show in New York City on June 14 with Kayed Almasarweh, the Watson and cognitive IoT solutions lead at IBM. The program, Leveraging IoT for Predictive Maintenance, will look at the combination of condition monitoring data collection and the analysis of that data via Big Data processing in IBM’s Watson.
Grabbing the Quick ROI
Before customers make the major jump into Big Data processing, they can enjoy an early return on investment (ROI) from predictive maintenance basics—the stream of equipment data that comes from sensors and is delivered to a condition monitoring system via the IIoT. ROI can be achieved more quickly if the company doesn’t have to set up the servers and configure the software. Service companies like RRAMAC can grab the sensor data over the internet and process it on remote servers. “When you’re not installing a bunch of software and spending time learning how to configure it in-house, it shortens the timeline to the ROI,” said Craven. “The initial investment is less when you don’t have to invest in all the development hours to get it going.”
The reduced investment allows companies that couldn’t otherwise afford to develop a condition monitoring system to reap the benefits of predictive maintenance. “The IIoT brings predictive maintenance to a whole new set of customers where it wouldn’t have made sense before,” said Craven. “A lot of companies can benefit from predictive maintenance even if it doesn’t make sense for them to do it on their own.”
Giving the OEMs Their Own Equipment Data
Not all predictive maintenance data needs to go directly to the end user. In some cases, the end customer is using a piece of equipment that’s not connected to a factory line. Examples can include a recycling machine or a rock crusher. The user doesn’t have the network to gather equipment data, so the equipment OEM can track the machine data and monitor the equipment’s health. “Sometimes, our customer is the OEM. The OEM gets the information. The customer may get the information as well, including the alerts,” said Craven. “We provide data to the OEM and if the OEM chooses, the OEM can provide the data to the customer.”
OEMs often sell extended warranties. But the OEM can only sell the extended warranty if the health of the machine can be monitored. “If you have a machine that requires maintenance, those machines can wear out quickly if they’re not maintained,” said Craven. “If the OEM is monitoring the equipment regularly and making sure the customer is doing regular maintenance, the OEM can extend the warranty knowing that it’s enforced. It’s just like Ford not supporting the warranty on a car that hasn’t had regular oil changes.”
Rob Spiegel has covered automation and control for 17 years, 15 of them for Design News. Other topics he has covered include supply chain technology, alternative energy, and cyber security. For 10 years, he was owner and publisher of the food magazine Chile Pepper.
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