Recently we took part in Implementing the Industrial Internet of Things (IIoT) Roadshow in Kraków and Wrocław. During the Keynote, National Instruments presented pragmatic reasons why is worth to talk about IIoT and what benefits it brings to the organisation – increased uptime, enhanced performance and improved design. Later, the considerations for the IIoT implementation were defined and discussed. As there are two key computing models in the IoT – Edge Computing and Cloud Computing, both were covered by NI.
In Edge Computing, data processing occurs close to the data source. In the industrial context, the sources are usually sensors used to measure some physical phenomenon regarding the monitored process. Using flexible hardware platforms, like CompactRIO, we can implement fast Data Acquisition, and then efficient Data Processing or even advanced Control algorithms. All of this can be done in single LabVIEW environment and deployed to real-time operating system or FPGA, which are combined on cRIO platform. To synchronize different nodes on our system, we can use built-in Time-Sensitive Network (TSN) features. This is an industry standard, based on IEEE 802.1AS, so it is also possible to synchronize with compliant hardware from other vendors. The synchronization is provided through standard Ethernet connection, which removes the need for additional synch lines and cables between the targets. Depending on the system configuration and network topology, we can archieve from less than 1 microsecond synchronization, down to hundreds of nanoseconds range.
Another important aspect of IIoT system is data management and storage. Even the simple system like single pump/motor monitoring can produce a few dozens gigabytes of data per day. Only a small portion of that might actually contain usefull information. CompactRIO platform flexibility again comes in handy in managing this data. We can implement advanced triggering algorithms to account for specific data capture conditions, like alert situations. We can also reduce the amout of data by providing analysis on the Edge, from simple insights to advanced machine-learing algorithms. Then we can store the data locally, using any required file format, or move it to the database, or even push it to the computing cloud for further processing.