Predictive maintenance leverages the power of big data to increase output, minimize downtime and address other key business drivers. Here’s how.
Predictive maintenance programs represent the next step in digital asset management. This data-driven approach to maintenance keeps lasers cutting, extruders extruding and forklifts moving through the plant floor longer than ever. In fact, compared to preventive maintenance, this addition to the manufacturer’s toolkit significantly reduces offline time for mission-critical equipment.
So why the new approach? Because real-time data changes everything. Legacy maintenance protocols essentially operate on guesswork—it’s educated guesswork, but the tests that lead to maintenance schedules take place in conditions that likely differ from those in the field. With the latest generation of analytics tools and data sensors, it’s now possible to make key maintenance decisions based on real-life, in-the-moment conditions within and surrounding assets.
“[Within a preventive maintenance framework,] you say, after 20,000 hours, we’re going to do an oil check, we’re going to do a bearing check,” said Luke Durcan, Director of EcoStruxure, Schneider Electric’s digital operating system for enterprise. “These well intentioned rules aren’t typically developed with the knowledge of specific operating conditions.”
Predictive maintenance systems collect and analyze vast data stores to determine when equipment failure is likely before it comes to pass. These systems can even calculate vital information like an engine’s remaining useful life (RUL). This analysis hits the sweet spot between unnecessary offlining at one extreme and costly neglect on the other.
How Predictive Maintenance Improves Industrial Operations
In an age of rapidly shrinking time-to-market goals, precise data analysis doesn’t just give you a competitive edge; it’s practically mandatory. Enter predictive maintenance. Here are just a few of the advantages of a cutting-edge asset monitoring system:
Predictive maintenance isn’t in every manufacturing facility yet, but the spread of IIoT-enabled machines in manufacturing and supply chain operations (this is the Industrial Internet of Things you’ve heard so much about) has sped up its growth considerably.
That just leaves one question: How do you get started with predictive maintenance?
Implementing a Predictive Maintenance Program
To be clear, you can’t go out and buy a “predictive maintenance machine.” Predictive maintenance is less a product and more a strategy, a digital approach to managing complex systems. Certainly, digital asset management does require certain equipment, but technology is only one part of the solution, Durcan said.
Graybar takes a consultative approach to predictive maintenance to understand where customers are today and where they want to be in the future. From there, a roadmap is outlined to implement the right solution that meets the facility and employees’ needs.
Introducing predictive maintenance can require significant changes in operations, complete with training and retraining programs; whatever it takes to get the processes down and the team ready to follow them. In terms of equipment alone, IoT-ready manufacturing systems need to generate data, and that tends to mean sensors of one sort or another.
“How these systems collect the data to predict the failure state varies by industry,” said Durcan. “Think about a thermocouple on a pump...it’s easy to predict [problems] because you look at the thermal performance of the pump. If it runs at 90 degrees, say that’s slightly above normal. At 110 [degrees], you say, ‘We need to do something about that.’”
Complex Digital Tools for Complex Manufacturing Systems
Of course, getting into the predictive maintenance game isn’t as simple as sticking sensors on existing manufacturing lines. You really need to understand the entire system, as well as individual components, to create an effective digital game plan, Durcan said.
Take that thermocouple, for instance.
“You need to know what the thermocouple is, and what the parameters of the thermocouple are,” Durcan said. “You need to understand the context around the asset being monitored. That’s really important.”
In fact, every instance of predictive maintenance will be unique, tailored specifically to the individual manufacturer’s broader business goals, production processes and equipment. There is no “one-size-fits-all” solution when it comes to data analytics.
“We look at what the client is trying to achieve from a business perspective,” said Durcan, describing the EcoStruxure approach to implementing data-driven maintenance. “We’ll talk to a client, engage and understand what the key business drivers are.”
Only then can they start to build the digital solution that works for that company; after all, digital solutions exist to solve real, concrete problems.
The Next Step Toward Predictive Maintenance
Just-in-time procurement changed our supply chains. Think of predictive maintenance as just-in-time equipment care. The rise of big data is moving all of our workplace processes into the fast lane with real-time analysis and trend recognition.
To learn more about predictive maintenance and other IIoT solutions, or to discuss the specific equipment involved, contact your personal Graybar representative. Don’t have one yet? Find your nearest Graybar branch and get in touch to start the conversation.