Technology has never advanced faster, with the global adoption of smartphones, self-learning and agile robots, affordable genome sequencing, and ubiquitous data storage. There have been impressive advances so far in the 21st century. Though some of the current innovations, such as 600 mph hyperloops, fully-autonomous vehicles, and artificial intelligence, are in the prototype stage, they're evidence higher quality devices, faster test times, more reliable networking, and almost instantaneous computing are quickly becoming reality.
According to National Instruments (NI), it's just as important to think critically about where we're headed, and how we'll get there, as it is to consider the expected benefits. The NI Trend Watch examines topics such as the mass deployment of the Industrial Internet of Things (IIoT), machine learning, and upcoming challenges in testing increasingly connected and electrified systems. The following is a summary of the five trends in NI Trend Watch 2018.
1. Three mandates to successfully manage IIoT efforts
Accenture estimates that 95% of companies will adopt the IIoT in the next three years to maximize uptime, optimize performance, and drive product and process innovation. In other words, implementing the IIoT is no longer about getting ahead; it's about not being left behind. Now, smart and connected "things" give companies opportunities for increased performance and lower costs, but managing these distributed systems is often an overlooked challenge.
Companies across all industries are adopting a new breed of disruptive platforms and ecosystems that will transform businesses into engines of innovation and growth by taking advantage of intelligent technologies. With IIoT technologies, they can harness the benefits of these state-of-the-art platforms to ultimately reduce maintenance costs and improve asset utilization. To successfully manage an IIoT strategy, companies must manage data, software configuration, and remote systems.
2. Progress of 5G set to disrupt test processes
5G signifies a generational transformation that will profoundly impact businesses and consumers globally. It promises an experience that many consumers are hungry for: faster data, shorter network response times (lower latency), instant access anywhere and everywhere, and the capacity for billions of devices.
Though test and measurement solutions will be key in the commercialization cycle, 5G is set to disrupt test processes because it requires a different approach to test than previous generations of wireless technologies. A platform-based approach that is flexible and software-configurable will be essential to the development of this ecosystem.
3. Breaking Moore's Law
Recent publications say Moore's Law (the observation that the number of transistors on an integrated circuit doubles about every two years) is dead. Though it may be experiencing some health challenges, it's not time to start digging the grave for the semiconductor and electronics market yet.
New computing techniques and new applications for existing technology continue to advance the capabilities for high-speed input/output (I/O) and processing. As previous architectural leaps, such as multicore processors, have shown, the keys to riding the wave are the software tools and frameworks that leverage the diversifying computing elements.
4. Vehicle electrification: Disrupting the automotive industry and beyond
Ten years ago, a fully-mechanical coupling between the steering wheel and the front wheels was common. However, the explosion of drive-by-wire technology, combined with government mandates toward fully electric powertrains, has changed this paradigm—and it impacts more than just the automotive industry.
The reliance on power electronics and electric motor drives adds complexity to control systems, and combining these control systems makes that complexity grow exponentially. Directly, these factors increase the complexity of vehicles. Indirectly, they create an immediate need for growth in infrastructure. Making it happen requires an interdisciplinary approach to building safe and reliable control systems among other needs.
5. Automating engineering insights with machine learning
Machine learning already has delivered beneficial results in certain niches, but it has potential for a bigger and longer lasting impact because of the demand for broad insights and efficiencies across industries.
As machine learning applications migrate from the consumer space alongside development platforms and converging IIoT edge node technology, business leaders are looking to engineers and the next wave of machine learning to help find uptime, yield, and efficiency improvements in a sea of analog Big Data.
As these advancements become a reality, it's important to keep pace with how these developments will make an impact across many industries.
Kyle Voosen is the section manager of data acquisition and control marketing at National Instruments. Edited by Emily Guenther, associate content manager, Control Engineering, CFE Media, eguenther(at)cfemedia.com.
Since joining National Instruments (NI) in 2001, Voosen has held leadership positions in multiple countries across marketing, sales, and engineering, and has worked with imaging and embedded control systems.
Voosen began his NI career as an applications engineer in Austin, Texas. He moved into product marketing two years later, gaining expertise in machine vision and image processing. He spent time at NI Germany, working as a technical marketing engineer, before returning to the U.S. to work in sales. Voosen moved to the UK in 2011 to lead LabVIEW marketing for Europe and regional marketing for Northern Europe. He returned to Austin in 2015 and currently is responsible for the strategy and marketing of NI's data acquisition and control products. He holds a bachelor's degree in Electrical Engineering from Rice University.
Keyword: Industrial Internet of Things
What are the top challenges engineers will face as more companies adopt an IIoT strategy?
Online: Read the full NI Trend Watch 2018 report here: