Bring your legacy systems into the future with IIoT

While manufacturing has long maintained its stature as the backbone of a healthy economy, the speed at which new technology develops is forcing the industry to a crossroads. Given its considerable history, it should be no surprise that standalone legacy systems are still commonplace throughout the manufacturing sector.

It’s estimated that there is $6.8 trillion worth of such equipment in the United States alone. But modern industrial manufacturing operations that have either upgraded to or launched with industrial internet of things (IIoT) technologies are benefiting from competitive advantages over those that solely rely on legacy systems.

The term “Internet of Things” was coined back in 1999 to describe a system where the internet is connected to the physical world via ubiquitous sensors. Specific and widespread applications were sparse outside of the tech space. Then, came the “Industrial Internet of Things,” which describes the usage of interconnected sensors, instruments, and other devices networked together with computers’ industrial applications, allowing for data collection, exchange, and analysis – all without human-to-human or human-to-computer interaction, which has never been more relevant than in the current and post COVID-19 business landscape.

• Read "Make automation safety more visible"

At its inception, many IIoT concepts were theoretical. As new smart tech was deployed in following years, the high cost of overhauling legacy systems and the apprehension of adopting yet-to-be proven solutions caused many companies to consider upgrading more as a luxury than a necessity. 

A lot has changed since then.

Growth and adoption trends

Today, countless examples and case studies demonstrate how deploying smart, connected solutions bolster bottom lines and optimize process controls through increases in efficiency and productivity. And even better, IIoT devices have evolved to the point where augmentation and the partial replacement of legacy systems can bridge the gap between manufacturing’s history and its future.

This evolution has catalyzed growth estimates of global spending on IIoT platforms for manufacturing from $1.67 billion in 2018 to $12.44 billion in 2024, attaining a 40% compound annual growth rate (CAGR) in seven years, according to Forbes. Building on these figures, the World Economic Forum notes that this “Industrial Awakening” is expected to generate $14.2 trillion of global output by 2030.

How to assess your operation's needs

Although IIoT has come a long way, planning to pilot new tech in a legacy environment can still feel like a daunting task due to the potential unfamiliarity with the various platforms. Shifting perceptions about augmenting existing systems from a technology issue to a business one is an effective way to help overcome these apprehensions.

Owners, operators, and managers should work together to identify which equipment would generate the most business impact by being augmented with IIoT devices. Any machines that would benefit the most by access to real-time data around production maintenance, quality, and inventory management are prime for upgrading.

IIoT sensors and the benefits of real-time data

Step one when augmenting legacy industrial equipment is to add sensors. Sensors like micro-electromechanical systems (MEMS), accelerometers, gyroscopes, and inertial measurement units (IMU), have become small enough with a reduced cost, making plant-wide deployments practical.

Different sensors can detect various anomalies in operational performance and identify patterns and trends from the data they collect to alert staff of potential failures, including but not limited to:

• air pressure differential
• temperature
• vibration data
• velocity and acceleration
• force
• pressure
• sound.

Analyzing this type of data is critical because downtime caused by machine failures can be costly for any industrial enterprise. Installing IIoT sensors to legacy equipment can reduce the risk by detecting performance deviations as they occur, allowing for pre-emptive maintenance to be deployed, saving the company money and headaches.

• Read "Overcome obstacles to remote monitoring"

Understanding the infrastructure needs of real-time data generation

Deploying IIoT devices generates large amounts of data, and for IIoT to be effective, the data must be analyzed and acted upon in real time. End users and manufacturers of IIoT technology are using several concurrent technological advances to deploy IIoT: sensors (which we covered above), Moore’s Law, and the ubiquity of bandwidth.

• Moore’s Law – Doubling the number of transistors in an integrated circuit every two years has resulted in small, cheap CPUs and memories.
• The ubiquity of bandwidth – IIoT devices that gather data need to send that data upstream for analysis. The ability to connect to a network is available everywhere. There is a wide range of ways IIoT devices can connect to the network, for example, copper or fiber optic cabling, Wi-Fi, ZigBee, and cellular, to name a few.

Network architects or network managers can consider several options in order to prepare their data centers for supporting real-time applications and the IIoT:

• Check your network. The network may be intermittently dropping packets. The rate of dropping packets may be low enough to not notice the impact on throughput, but a dropped packet in a network that needs to respond in real time may wreak havoc on latency.
• Use a low-latency infrastructure. Different media have different latencies. For example, the latency through an optical fiber link may be several hundred nanoseconds, whereas the latency of a 10GBASE-T link is approximately 2-2.5µS. Switches, routers, and servers all have a latency associated with them. You can improve a data center’s responsiveness by selecting lower latency equipment. This may not seem like much of a difference but, depending on the network’s architecture and the number of hops, the latency of the media could have an impact. Exploring the media used for the network is one of the easier ways to lower latency.
• Upgrade the network’s speed. Although increasing the network’s speed does not shorten the media’s inherent latency, it does increase how fast packets are reassembled.
• Adopt a spine-leaf architecture. A traditional network architecture has three layers: access switches, aggregation switches, and core switches. You could adopt a spine-leaf architecture where an entire layer of switching does not exist, thereby improving latency and responsiveness.
• Implement a time-sensitive network. A time-sensitive network (TSN) has mechanisms to control three aspects of a network: jitter, latency, and guaranteed bandwidth. Historically, TSNs have been proprietary and somewhat costly. However, organizations such as IEEE have created standards for implementing TSNs with support from various vendors. With a TSN, the data that requires action in real time takes priority over the non-real-time traffic.
• Explore edge computing. To shorten latency, locate the computing resources closer to the IIoT devices that are generating the data. Edge computing is a trend that is a contrast to locating computing resources in a few very large, hyperscale data centers. (Panduit’s Pre-Configured Micro Data Center is an example of edge computing technology.)

Conclusion

Data shows that IIoT was projected to grow at an exponential rate before COVID-19, and the effects of the virus on our society is only speeding up digital migration. The information in this article will give you a solid knowledge base to use when considering which machines to augment, and options to consider when choosing the right solutions to meet the needs of your operation. The way things are trending, many manufacturers essentially have two choices: Bridge the gap between legacy and IIoT systems or get swallowed by it.