Embedded World is one of the world’s largest gatherings for embedded technology, bringing more than 30K people to Nuremberg, Germany from across the globe to experience the latest tech impacting the embedded industry. Here's a summary of what happened at the event, though our NI lens.
So why is time-sensitive networking so important for embedded this year? We address this directly in our TSN keynote at NIWeek 2016 last fall. Here’s an updated transcript of that keynote, featuring our own Jamie Smith and Todd Walter, Intel’s Kevin Stanton, and Cisco’s Paul Didier. You can also watch the keynote above.
The internet of things (IoT) continues to capture headlines around the globe, as technology companies everywhere begin to leverage the opportunities and competitive advantages connected devices bring to business. 2017 will be a big year for embedded monitoring based on the industrial internet of things (IIoT): millions of connected monitoring devices providing big data about machine/grid performance. Embedded World 2017 in Nuremberg will give us a great look at how companies are leveraging the IIoT, and how the industry is adapting.
We’re bringing an unprecedented level of interoperability to operational technology (OT) and informational technology (IT).
Our open, software-centric platform forms the bedrock of the Condition Monitoring Tested at the forefront of machine learning. By adding SparkCognition’s data analytics to the mix, the testbed processes big data much faster - allowing you to proactively avoid unplanned equipment fatigue and critical asset failure faster by having advanced insights into equipment health and remediation solutions. The technology supports some serious operational improvements:
Increased operational efficiencies
Decreased maintenance costs
In a new age of big analog data, machine learning is a primary way to harness information. The ability to collect raw data and derive insights to improve operations, equipment and processes offers huge cost savings and competitive advantages as data warns operators about component failures before they occur, identify sub-optimal operating conditions, and assist with root-cause analysis.