Why edge computing is so crucial for IIoT
The invention of the Printed Circuit Board (PCB) in the 1950s changed the world of automation. Prior to the PCB, electronic circuit boards were assembled exclusively by hand, a laborious process that greatly limited global production.
Today, says Michael Schuldenfrei, corporate technology fellow at OptimalPlus, industry is experiencing yet another revolutionary leap with the introduction of instrumentation in the manufacturing process and the use of edge computing.
Instrumentation of the manufacturing process involves numerous sensors and microcontrollers which can subtly alter manufacturing conditions in response to environmental conditions detected by the sensors. These sensors produce large quantities of data, but the microcontrollers cannot respond directly to the data produced.
Both the sensors and microcontrollers used in manufacturing instrumentation are basically small networked computers. The sensors send their data to a central location where the data is then analysed. These small, autonomous computers are not monitored by humans in real time and are part of the Internet of Things (IoT). More specifically, in a manufacturing context, they are Industrial IoT (IIoT) devices.
IIoT devices are used in any number of contexts to do jobs that would be difficult — if not impossible — for humans to do reliably and/or accurately every time. Consider, for example, weld inspection. Welding is an integral part of many electronics production lines and critical to the functionality and durability of the final product.
Unfortunately, manufacturers are being asked to perform welds on increasingly smaller components, with increasingly tighter constraints. In order to protect components, welds must be performed at the lowest possible heat and with the smallest possible electrical charge.
IIoT devices that might help refine this process include heat, voltage, and pressure sensors to help determine the minimum amperage necessary to perform a weld in the current environmental conditions. IIoT cameras may also feed Machine Learning-based visual weld inspection systems to verify that welds are satisfactory, even when they are far too small for the human eye to see; and this is just for starters.
Manufacturing instrumentation can make any manufacturing — not just electronics manufacturing — more accurate, with fewer production errors and requiring fewer people involved. Unfortunately, this instrumentation isn’t easy, especially given the complexities of the modern manufacturing supply chain.
Information Technology (IT) teams have been making use of instrumentation for decades. It doesn’t cost as much to build sensors into software as it does to build them into hardware. As a result, operating systems, applications, and IT equipment of all kinds are absolutely littered with sensors. Because of this, IT teams have been struggling with the amount of data they produce since before the modern microcomputer existed.
In the real world, any instrumented infrastructure produces way more information than a single human can possibly process. Even large teams of humans cannot be expected to comb through all the data emitted by even a modest IT infrastructure. Entire disciplines exist within the IT field dedicated to making the data emitted by IT instrumentation understandable. Technologies and techniques range from simple filters to sophisticated Artificial Intelligence (AI) and Machine Learning (ML) techniques.
Until recently, this was good enough for most IT teams. Information would be collected and sent to a central location, numbers would be crunched, and only the important data was forwarded to systems administrators. If this took a few seconds or minutes, that was okay; a brief IT outage was generally acceptable.
But as organisations around the world became more and more dependent upon their IT, the acceptable amount of time it took to act on instrumentation decreased significantly.
