How IIoT will Change Industrial Production Processes in Industry 4.0
IIoT or Industrial Internet of Things is a special discipline within the larger concept of IoT. The use cases in IIoT relate to the performance of plant, machinery and equipment. The performance data is obtained through sensors, cameras, etc. to have them analyzed using machine learning, data analytics, artificial intelligence, etc.
Below are a few of the actions that IIoT or Industrial Internet of Things will perform in the industry to change the way production takes place to give way for Industry 4.0:
Industry 1.0, 2.0 and 3.0 focused on industrial production, mass production and automation respectively. Automation led to the idea of preventing machine downtime to ensure seamless production and to do maintenance work, as and when the need arises.
Predictive Maintenance uses sensors and cameras to identify errors that could lead to potential failures. The service team can step in to fix the issue, even before the failure occurs to minimize errors and to continue production without any disturbances.
The data can also be shared with the cloud to be accessed by the factory owner, service teams, manufacturer of the machinery, etc. This may also enable EaaS (Equipment as a Service) where the equipment manufacturer can offer his industrial machinery as a service to manufacturers instead of selling them outright.
When all these data derived from the sensors and machines are used for analysis to arrive at actionable insights, it’s called Predictive Analytics. Combined with AI, Predictive Analytics can throw a lot more insights into industrial production processes that we had hitherto not known about.
The Digital Twin technology simulates how the actual equipment is currently functioning in real-time. A replica of that tool, equipment, machines or machinery performs the same tasks as the real-world machinery.
With Digital Twin, you can clearly know what’s going on inside the machinery. Digital Twin is essentially a visualization tool that can facilitate visualization of the inner workings of a machine as well as of the whole plant or factory.
The data then gets subjected to analysis to get useful actionable insights into the machinery to prevent downtime, wastage and to improve the performance of machines.
With the use of sensors, etc., we have been able to learn when a failure, blockage, leakage, etc. happens. Technologies of the past learned of things only in hindsight. IIoT, Industry 4.0, ArtificiaI Intelligence and Machine Learning can predict the course or flow of events with the help of data from past and current events.
Process Optimization enables plants or factories to anticipate equipment failure, reduce wastes and improve product quality. To accomplish these goals, it uses an assortment of services, concepts, tools and technologies as predictive maintenance, predictive analytics, automation, digital twin, simulation, etc.
So, process optimization is the effort taken towards the elimination of production disturbances, inefficiencies, wastage, blockage, leakage, etc. to prevent them from happening to increase efficiency, productivity and quality.
Optimize your industrial production processes by emailing us at firstname.lastname@example.org and also learn about us at www.twilightitsolutions.com to ease your transition during your upgrade from Industry 3.0 to Industry 4.0.