The Internet of Things (IoT) has witnessed rapid advancements over the years. It has also been introduced into the manufacturing world as industrial internet of things (IIoT). The latest production technologies such as electric machinery and automated equipment rely on real-time data analysis using IoT. The improved computing capability and growth of big data in IoT has facilitated the connection of large systems with smaller components to drive industrial automation.
IIoT facilitates seamless collection of data and quick analysis of the gathered information to produce actionable results. It enhances the functioning of robotic systems and automated assembly lines, and components like pneumatic valves and actuators. IIoT increases not only the uptime of the industrial organizations, but also their efficiency and productivity.
In modern production environments, IIoT enables quick assessments through its intricate network of sensors, which collect data from the components. The gathered data is analyzed and necessary actions are taken to maximize productivity and minimize downtime. The benefits of integrating IIoT in the production environment facilitates higher yields, enhanced quality, system integrity, inventory control, and lower cost of ownership, energy optimization, and remote access to analytic information.
Even though the adoption of IIoT has proved beneficial to the organizations, a complete IIoT implementation is not without challenges. The IIoT manufacturing applications act on real-time data from advanced components with dedicated device-level intelligence. Besides, the edge and cloud-based computing require advanced infrastructure capable of processing big data solutions.
Leveraging embedded intelligence at device level offers several benefits to the users. It gathers the relevant data from smart pneumatic devices. The programmable logic controller (PLC) program remains intact as the diagnostic and prognostic information gathering is carried out at the device level. The availability of the prognostic data enables predictive maintenance to be carried out when required.
Complete integration of IIoT will require smarter devices in the manufacturing environment. Device-level analytics can sidestep PLC, eliminating the need for any changes in the PLC programming. The integrated web server and LCD enable the personnel to remotely keep track of the device health via an application on a mobile device. The applications are designed to report critical issues and component failure. Also, device-level intelligence supports edge and cloud computing and does not require high-level computing infrastructure.