THE PATH TO IIoT
Industrial Internet of Things
A look at the path to the Industrial Internet of Things (IIoT) and how it might impact the industrial world, from environmental monitoring to worker safety.
The five-horsepower steam engines of the early 1700s may not have packed a lot of punch, but they changed the mining game by allowing miners to dig deeper than ever before.
The next major inflection point came in the late 1800s, when steel production became cheap enough to lay massive networks of railroads, exponentially increasing the speed and reach of global commerce.
Industrial computing emerged in the 1960s, introducing field devices that could communicate with distributed control systems, which would later evolve into full production scheduling systems.
Today, we find ourselves on the cusp of the fourth great leap in productivity and optimization – the Industrial Internet of Things (IIoT). IIoT elevates data analysis and process control out of the silos of individual systems or sites.
Engineers empowered with IIoT will have awareness and control beyond what they ever dreamed possible before. Using machine learning and AI, they will have the keys to the process optimization kingdom, marrying together staggering amounts of data across multiple sites of operation to synthesize into actionable insights.
Let’s get beyond the buzzword salad to look at some potential use cases for IIoT for the industrial world:
Today’s Pain: Mining, wastewater, and oil applications all run the risk of contamination, not only of their surrounding environment, but of the air that the workers breathe, making industrial work many times more hazardous than military or police work.
With sensors monitoring soil, air, and nearby bodies of water for changes in quality, the data can be aggregated and crunched by artificial intelligence to correlate contamination with changes in processes or equipment performance. This goes beyond mere reaction to damage done, but enables powerful root cause analysis to predict and prevent future environmental impacts. This also applies to worker safety, especially in mines where air quality is a going concern.
Today’s Pain: Aside from air quality concerns, proximity to dangerous equipment, explosive material, and poisons (like tanks filled with cyanide for ore separation) put workers in harm’s way.
Worker location tracking via sensors in hard hats can help safety officers respond more quickly to accidents. This can also help AI predict when workers will be in hazardous locations, and for how long. The ability to visualize the location and movement of individuals makes for more effective evacuation drills, as well. Shaft stability sensors can provide better predictions about the hazards of collapses in mines.
Predictive Maintenance for Reduced Downtimes
Today’s Pain: Pump failures and other equipment breakdowns are painfully expensive, as operations hemorrhage money as a result of downtime.
IIoT-enabled instrumentation will communicate its uptimes and downtimes to a cloud server, where trends can be analyzed by machine learning algorithms to generate an AI-optimized maintenance schedule that minimizes downtime and extends equipment lifespan. By connecting data from similar instrumentation across multiple sites, the AI has more data on which to feed, making its predictions more accurate as time goes on.
Big Data Insights
Which machines consume more power than they’re worth? Which of our transportation routes is most fuel efficient? Who is the most reliable equipment manufacturer? These are questions that can be answered by the large amounts of data collected by IIoT-enabled operations. AI algorithms are able to make correlations and inferences from disparate data that are beyond human capacity.
The Big Challenge
Why don’t billion-dollar multinational industrial corporations just take the leap and join the 21st century?
Much of the inertia in the industrial world comes from the inability to see the long-term financial benefits of upgrading operations and equipment. There’s an intimidating up-front R&D cost to trying new things. Even when commodity prices soar through the roof, companies will often opt to store away profits as a hedge against future volatility rather than invest in innovation.
A risk-averse culture in many of these companies is another barrier to progress. Few decision-makers want to put their neck on the line for an R&D project that isn’t 100% guaranteed to pan out. Unlike Silicon Valley’s “fail often, fail quickly” attitude, which keeps the pace of progress blazing along at blinding speed, the default move of the industrial world is to safely walk on well-worn paths.
One way or another, however, the industrial world will be dragged kicking and screaming out of the 1960s.
Red Meters and IIoT
We have an in-house team of experienced software developers who are releasing a huge upgrade to the Red Meters system on March 31, 2021. (Click here to register for the digital launch event.) By creating an architecture designed for scalability and extensibility, we’ve developed more than just an interface for an instrument. We’ve developed a platform that will support integration with IIoT analytics and multi-site instrumentation management.
As we touched on in the human-centered design article, our product development ethos is to design around the core needs of the industry, not just react to short-term demands. Innovation is a core value at Red Meters, and we’re committed to being the tip of the spear of the next Industrial Revolution.