How Standalone Machine becomes SMART

 As the Internet of Things comes of age, edge computing is taking some of the essential data processing and analytics work from the cloud and bringing it to the intelligent edge.

Today, in almost all cases, IIoT and AI work together in cloud computing—because the vast computing power required to train real-world ML models is available only in the cloud. Data from Edge devices is transmitted back to a common server in the cloud where it is analysed and stored, and actionable insights and analytics are sent back to the device. The true benefit of edge computing is realized for devices that produce vast amounts of data that can be best processed more efficiently at the edge instead of transferring all of the data across a network to the cloud.

Similarly, there is analytics possible for standalone machines too. The benefits of having analytics on the edge and the panel are becoming even more critical as IIoT and IOT projects mature. Such is the scenario with machine builders. There are many legacy machines with legacy automation systems in the market and end customers are looking to update those systems by adopting new IIoT technology without connecting them to the cloud. An AI based logic controller makes it possible to take legacy systems and machines into the new era of the intelligent edge to make use of real-time data without connecting to any cloud on the server.

For instance, consider legacy automation systems that are set up to manage the logic to produce an end result, such as a capsule filling machine. The system has a fixed job to do; the system is not using advanced IIoT concepts such as machine learning. However, in order to do something more and expand the system, you can attach an edge device to collect the variables these automation systems are using or just read the application logic from the PLC . By collecting that data in a AI-based smart controller, normalizing it, and then using it for running applications you can create a more advanced system to predict failure. 

AI based Logic device and strong controller platform is the framework that makes it possible for multiple applications to use the data in an efficient manner. This controller access the logic of applications, written in PLC in the panel to simplify and speed up the process. 

Once the AI-based Logic system gathers enough samples accessed from the controller, it can also identify a solution, and create a full feedback loop to the same controller. Power of data computation gets on to the controller in the panel, reducing the latency and empowering faster responses. This will help stop critical machine operations from breaking down or hazardous incidents from taking place, allow users to build an adequate security and compliance framework that is essential for enterprise security and audits.

One of the practical concerns around IIoT adoption is the upfront cost due to network bandwidth, data storage, and computational power.AI based computing at the controller level can locally perform a lot of data computations at the machine, which reduces the final costs of an overall IIOT solution.

No matter what the problem might be, the same cloud and sever isolated AI device can use a suite of services such as local computation, machine learning, predictive analytics, faster decision-making, and various artificial intelligence algorithms that allows a standalone machine to become a smart machine.

It's possible