Artificial Intelligence

Artificial Intelligence

Do a simple Google search on the key elements needed to enable Industrial IoT and the amount of information obtained is staggering. The topic of AI netted more than 2.3 trillion articles on its own. The volume of articles written around Artificial Intelligence suggests that momentum continues to build across the Industrial Automation industry as many companies strive to create intelligent manufacturing facilities that unify OT and IT data to translate it into actionable datagrams of information. Armed with these new insights, better decisions can be made to improve manufacturing throughput, improve product quality and lower operating costs.

These Industry 4.0 intelligent factories built to utilize AI technology will need to sift through trillions of datasets obtained from a host of intelligent sensors and actuators, PLC’s, DCS, and robots. To harness all this OT data requires a new approach to data sharing that starts with the adoption of the Hub & Spoke architecture required to successfully implement Industrial IoT so that every level of the Industrial Automation pyramid can publish and subscribe its data to a Unified Namespace (UNS). Once all information is mapped to a MQTT format and transferred to the Unified Namespace, then a common data framework is achieved where both IT and OT data is freely shared across all facets of the enterprise. This now unlocks the full potential and impact of a AI productivity toolset residing in the Cloud or on premise.

The AI Productivity engine works to convert every datagram received into actionable information. As it gathers more data over time, it is now able to suggest new operating conditions by working with the factories Digital Twin simulation model to further optimize its performance. As these results are displayed on the HMI panel to the production supervisor, a decision is made to send the new performance parameters to the production equipment. Once this permission is granted to push the new performance targets across the fabric of Intelligent sensors and actuators, the equipment re-sets itself on the fly to achieve the new operational performance. Within a few seconds of the implemented change, the factory begins to show an immediate increase in production throughput. After more time, the factory benefits further from improved product quality and lower operating costs.

While AI productivity tools are a big part of providing value in an Industry 4.0 enterprise, AI and Neural nets are also being adopted at the Field Level into vision cameras and Intelligent motion control drives and solenoids. This combination of technology will provide new capabilities to allow for customized production runs for consumer and the ability to re-train a robotic cell to accommodate a variety of production tasks. Together, these technologies will allow the curation of personalized medical pill or vitamin packs, processing individualized dietary meal plans, and even re-directing agriculture or  farm equipment to seek out and eradicate certain types of weeds or insects to avoid crop damage.

New use cases will emerge over the next decade to further utilize the full potential of AI enabled algorithms. Some of these new use cases will combine AI technology with Intelligent sensors and self-aware actuators. This dynamic combination of capability will usher in a new way of thinking as we migrate from Industry 4.0 to the Industrial Metaverse.