By now, you’ve probably heard plenty of chatter about the IIoT, though adoption is still lagging across the industrial field. The benefits are hard to ignore, and it’s only a matter of time before the idea of machines talking to each other becomes as old hat as mobile devices on the plant floor.
The Industrial Internet of Things is a game changer when it comes to equipment maintenance. It all boils down to better data (and more of it), along with the IIoT trifecta: sensors, analytics, and machine learning.
As far as maintaining your equipment, the IIoT makes it easier to accurately predict imminent failures—and prevent them from happening before they shut down your production line to the tune of $22,000 per minute.
Predictive and preventive maintenance are two areas that can be significantly improved via IIoT technology. Rather than building a calendar-based maintenance program solely on the recommendations in equipment manuals, IIoT technology allows you to use real-time data and analysis to tailor more effective preventive and predictive strategies.
It all starts with:
Sensors are the workhorses upon which next-level data analysis is built. In the industrial world, sensors can provide continuous reliable data on factors that can predict failures such as vibration, sound, temperature, pressure, and strain. You can also use sensors to monitor things like power consumption and production rates.
In terms of maintenance, sensors simply allow you to monitor the health of your assets more accurately—in real time. They provide better information that creates a more complete picture of not only how your equipment is performing, but also how it’s holding up.
And while equipment sensors on the plant floor aren’t exactly new, the latest generation of sensors takes data to the next level. Beyond measuring system variables, new smart sensors can process the data and monitor their own internal diagnostics.
So what happens with all of that data that the sensors are collecting?
The immense amount of data collected by sensors can be overwhelming, and of course, there’s the matter of deciphering it all. Make no mistake, this is too much for any human brain to handle.
With the IIoT, the sensors on the plant floor are connected to a software platform, through which the data is processed. Smart, efficient software platforms have the ability to process data, monitor trends and offer actionable insights.
Having a maintenance goal in mind will make it much easier to mine for specific information. For example, measuring performance interruptions can help you determine if and when a part is going to fail.
Consider a predictive maintenance example in offshore oil processing. The performance of the many valves in a rig can be monitored—remotely—by recording the time it takes each valve to open and close. This data can then be analyzed to reveal which valves are sticking and in danger of failing.
Most importantly, action can then be taken to avoid unexpected valve failures that cause unplanned shutdowns.
“We need to embrace the reality that when it comes to processing data and predicting failures, machines are smarter than us.”
Machine learning is essentially smarter analysis: predictions that adjust without human input to be more accurate as more data is collected.
It starts by defining an outcome you’re trying to predict, such as equipment failure. (Beyond failures, outcomes may be things like saving energy or increasing productivity.)
Using algorithms, the software “learns” the factors that are directly related to failure. Unlike traditional analysis, these algorithms are actually fluid, meaning they improve over time as more data is gathered. Adjustments are made automatically to increase accuracy based on the actual outcome of predictions that have been made.
This is especially helpful when you don’t know what factors are causing the failure of a particular piece of equipment. Machine learning essentially boils down to the ability of a machine to learn exactly what’s causing a failure and predict future failures with incredible accuracy.
The value of the IIoT in industrial applications such as manufacturing is indisputable, especially when it comes to areas like predictive maintenance. So what does all of this mean for you?
Think: less guesswork, more accuracy, and machines that do the complicated data analysis for you. It’s all about machines talking to each other, learning patterns and trends, and making highly accurate predictions.
It means you can base your predictive and preventive maintenance schedules on the actual condition of your equipment rather than generic recommendations pulled from equipment manuals.
We need to embrace the reality that when it comes to processing data and predicting failures, machines are smarter than us.
While implementing IIoT technology may seem daunting, you’re likely already much closer than you think. Need some guidance on first steps? Consult Plant Engineering for some tips on getting your maintenance program ready for the IIoT.