Why Use IIoT?

Industry 4.0 is already here, and industries ranging from mining to manufacturing are realizing the benefits of integrating a host of next-generation technologies into their systems. The problem that many companies face, however, is figuring out where to start.

Should we invest in cloud computing for big data analytics? How about augmented reality (AR) and other immersive visualization tech? Or maybe machine learning (ML) to add a layer of intelligent automation to our processes?

All of these solutions require one key prerequisite: data, and lots of it. In many ways, Industry 4.0 boils down to collecting data about physical conditions from sensors and then using that data in creative ways to solve real problems and find avenues for optimization. Just like today’s tech giants gain their strategic advantage from their massive online datasets, tomorrow’s leading industrialists will rise to the top because they’ve figured out ways to harvest, process, and strategically implement their data.

That’s why the industrial internet of things (IIoT) is the first step in the transition towards the Fourth Industrial Revolution. This new class of smart devices is defined by two traits: network connectivity and data collection. Many of us are familiar with the way IoT devices in home automation collect data, such as smart thermostats that read temperature or voice assistants that process speech. Similarly, IIoT devices use sensors to harvest data from industrial equipment, monitor network performance, and give us a clearer picture into what’s going on at the shop floor level.

In this article, we’re going to cover some of the major use cases for IIoT, including anomaly detection, asset management, and improved automation. Keep in mind that improved data pipelines underpin each of these uses.

Use Cases for IIoT

One of the most common examples of IIoT at work is anomaly detection, a data science practice that looks for patterns in a data set to detect outliers. Applying this technique to machine monitoring lets us train a model to predict when the equipment is likely to break. For example, an IIoT device can measure an engine’s vibration, and, if it starts getting readings that are outside the normal range, it can automatically ping a technician to take care of it.

This has a few immediate impacts. First, anomaly detection minimizes unplanned downtime because our machinery is less likely to break out of nowhere. In this case, “if it ain’t broke, don’t fix it” no longer applies; we’re better off performing preventative maintenance when our IIoT device alerts us about the problem.

Not only does this prevent costly service calls, but it also opens the door to remote diagnostics. By analyzing sensor data, an expert can figure out the problem and plan a solution from anywhere in the world. For instance, a technician on the floor can work directly with an equipment’s manufacturer to troubleshoot the problem.

A second major use case for IIoT is asset management, especially in remote locations or across complex, distributed systems. Rail and utility companies, for example, operate over large distances. Before the IIoT, it was difficult, if not impossible, to get an accurate, high-level overview of their status. By integrating IIoT technology, they’re able to centralize their data, run analytics, and view metrics from a dashboard.

Essentially, IIoT is perfect for asset management because it combines its two major strengths, connectivity and data, to solve a problem. Not only do we now have access to more granular data than ever before, but we’re getting it in real-time.

The last use case that we want to highlight is improved automation. This is the heart of the smart factory. When we fully integrate IIoT into a workflow, we find that our machinery is constantly collecting data, sharing that data over machine-to-machine (M2M) communication protocols like MQTT or OPC UA, and using that data to drive smarter processes. This lets us achieve an unprecedented level of overall equipment effectiveness (OEE).

One of the most promising applications is edge computing, where we run our data processing as close to the data’s source as possible, often by leveraging a machine learning model. For example, an IIoT gateway can receive messages from a network of sensors, crunch the numbers in real-time on an embedded GPU, and then send commands to a robot. This is ideal for real-time applications that are too latency-sensitive to interface with the cloud, for keeping private data on the local area network (LAN), and for devices with constricted bandwidth, such as those operating over a cellular network.

Conclusion

While it’s important not to dismiss the engineering feats and dedication required to pull off a successful IIoT strategy, we also can’t overstate the fact that this is just the beginning of the rise of IIoT and that companies who make the investment stand to gain a significant competitive advantage going forward. The use cases above are a great launch point, but the truth is that the potential for IIoT goes well beyond them.

New business models, such as product-as-a-service, are starting to pop up. Take air compressors as an example. Instead of buying a Kaeser compressor, customers can now pay for cubic feet per minute (CFM) usage. Not only does this benefit the customer, but it also opens new revenue streams for the manufacturer.

It’s all thanks to IIoT. Connect with me today to start building a blueprint for your own IIoT implementation.

Image References:

Photo by Science in HD on Unsplash

Originally published at https://www.linkedin.com.