Smarter control on the factory floor: Top tips to get started with smart devices
22 July 2020
From smart devices up to artificial intelligence, the discussion around implementing IIoT and Industry 4.0 is picking up speed, especially as a result of increased computing power and the availability of growing amounts of data.
This is further accelerated by the increased use of equipment with IoT features, as well as the introduction of artificial intelligence on the factory floor.
Machine controllers equipped with adaptive algorithms offer enormous potential for further developments such as predictive maintenance and efficient networked production which are necessary within the framework of Industry 4.0. In this context, manufacturing companies are realising that these developments give them the opportunity to increase overall equipment effectiveness (OEE), reduce costs and increase productivity.
Gartner predicts that by 2022, more than 80% of enterprise IoT projects will include an AI component, up from only 10% today. The Internet of Things is all about connected devices responding to circumstances based on the data that they collect. After all, without an efficient way to interpret the data and to define actions, the sensors are just collecting information that can’t be used.
When implementing smart devices for IIoT or Industry 4.0, many manufacturers face a situation where they are restricted by their existing infrastructure of legacy machinery and plant, lacking standardisation of system architecture. So, defining a process or starting point for implementation can be challenging. Here are 5 tips to help you get started:
1. Define the problem you need to solve
One of the biggest challenges that manufacturers face is that they don’t know what problem they want to solve. But how can you define the problem without data? The solution is to start collecting and cleaning data first. You can then begin to obtain information from the data, visualise it and see where the areas of improvement are.
2. Define how to access and make the best use of your data
The machines within a factory are a potential source of valuable data. But how can users access and analyse the data that a machine could provide? How can a manufacturing plant then make the most effective possible use of this data? Ask yourself if you have enough data, which is the most relevant and how will it be used? How much will the infrastructure cost?
3. Deploy a system that enables monitoring machinery or plant effectiveness
One of the first steps we recommend to manufacturers that are beginning their Industry 4.0 journey is to deploy a system that enables them to monitor machinery or plant effectiveness. These types of solutions can be used to monitor productivity and downtime. Whilst being relatively simple and cost effective to deploy, systems like these provide valuable line level information and permit more informed decisions about possible areas of additional investment.
4. Ensure real-time communication between devices
Getting the right data from the 'grass root' level of the manufacturing process is essential when creating the factory of the future. Real-time communication to and from field level devices, for example, opens vendor protocols like IO-Link, allowing sensors and actuators to exchange data with the machine controller. Bi-directional communication is established so parameters can be transferred from the controller to the devices and the status can be read. Sensors and actuators can communicate more than simple on/off signals or analogue ranges. They can provide advanced status and diagnostics information communicating with the controller about how they are performing. Furthermore, the controller can also change the sensor’s parameters, creating the ultimate in flexible manufacturing.
5. Make the most of your smart devices and the possibilities of AI
Once you’ve established real-time communication between the devices, the field devices can be monitored and corrected before they malfunction and cause a line stoppage. Another level of predictive maintenance can be achieved with artificial intelligence at the Edge. AI at the Edge using a machine controller with an AI library, for example, allows companies to collect, process and react to data collected at line level in real time. With this approach, the machine is collecting all the data. Although the scope of the data remains relatively large, organisations need less resources in terms of hardware, communication infrastructure or processing capabilities at enterprise levels.
Latest solutions from Omron
Omron has recently released the DC 3-Wire E2E NEXT Series Proximity Sensors, which boast the world's longest sensing distances, newly equipped with IoT features. They help improve facility operation rates by preventing unforeseen facility stoppages and reducing facility downtime. Omron also provides AI capability in a machine controller that operates at the Edge of the machine, enabling predictive maintenance in real-time. In addition to microsecond response time to potential failures, the risk of potential security threats linked to the use of AI can be controlled more easily than with AI on the cloud.
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