SPECIAL FOCUS Smart Devices y


Read the latest industry news online at automationmagazine.co.uk


Smarter control on the factory floor T


In this article, Omron provides five tips to help you get started with smart devices on the factory floor


he discussion around implementing IIoT and Industry 4.0, from smart devices up to


artificial intelligence in the factory is picking up speed, especially as a result of increased computing power and the availability of growing amounts of data, and 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 networked, efficient production that 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


per cent of enterprise IoT projects will include an AI component, up from only 10 per cent 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 canot 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 five 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 do not 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 start obtaining 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, do I have enough data, and which data is the most relevant and how will it be used? And how much will the infrastructure cost?


3. 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, open vendor protocols like IO-Link, allows sensors and actuators to exchange data with the machine controller. Bidirectional 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.


4. 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.


5. Make the most of your smart devices, and the possibilities of AI


Once you have 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, for example using a machine controller with an AI library, allows companies to collect, process and react to data collected at line level, in real time. In 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.


CONTACT:


Omron Web: omron.co.uk


8 June 2020 | Automation


automationmagazine.co.uk


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44