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The edge vs the Cloud and AI relationship
Cloud computing and edge computing are two technological advances that are playing a key role in the enhanced development of machines.
Cloud computing – the storage, management and analysis of data that is stored remotely on a server either locally or on the Internet – has become commonplace in a relatively short time. Although it has proved invaluable in many circumstances, is it always the best solution? In particular, is it the best solution for the production line? Recently, another promising alternative has emerged: edge computing.
Edge computing enables data storage, applications and analysis to be carried out at the edge of a machine. Whilst there are various interpretations about what edge entails, data mining at the edge can be compared to a spinal reflex. Lines and devices are monitored with real-time sensors, and data at the machine level can be processed in microseconds. A machine’s condition can be monitored in real time, but the data volume is limited. Real-time data processing at the edge also enables an immediate response.
Industrial manufacturers need to think carefully before deciding on a solution and consider the recent arrival of new solutions involving artificial intelligence (AI) and machine learning (ML). Although AI offers some great potential benefits, care needs to be exercised before incorporating it into industrial applications. All too often, companies can be eager to start implementing and using it without being fully aware of the challenges they could face.
So, what are the key issues involved in deploying AI and in determining how AI can improve a production line or a process, and if cloud computing or edge computing should be implemented?
Issue 1: What’s your main challenge?
The biggest challenge that companies face is that they often don’t know what problem they want to solve. Some of them aren’t measuring any data yet, so even though they might be keen to implement AI, this will prove difficult. The solution is to start collecting and cleaning data first, before even thinking about introducing AI. You can then start trying to obtain information from the data and then begin visualising this in a smart way. This will help your company to start realising a range of benefits.
The next step is to consider implementing AI. You can apply AI at various levels, depending on the problem you want to solve. For instance, if you want to compare the performance of two factories, you can gather the data and put it into the cloud (inside or outside your enterprise), and then you can compare and analyse the data and start to draw conclusions.
At the other end of the spectrum, you might want to analyse the performance of a machine that isn’t meeting your full specifications. This is therefore a completely different problem as it can take hours or days to collect the data and analyse it. Instead, you need a solution that will run in your machine and that can identify a low-quality pattern. This is where edge computing is very useful.
The main challenge remains: what problem do you want to solve and what are the most effective tools?
Issue 2: How can you best use the data you have access to?
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, especially when introducing AI to enhance its capabilities? The key questions that need to be addressed from the start are:
• The data: Do I have enough data – and if so, which data is the most relevant and how will it be used?
• The infrastructure: How much will the infrastructure cost?
• The outcomes: What problem do I really need to solve and what increase in efficiency can be achieved by the use of Cloud or edge computing?
As mentioned, one of the potential drawbacks of using cloud computing in the factory is that it can be difficult to gain a true picture of the real-time performance of equipment. There is no way of looking inside the machine to see what is happening.
However, in edge computing within an industrial manufacturing environment, you can look at the actual process within the machine. Real-time data processing at the edge enables an immediate response to an abnormal situation in a process. With AI at the edge, manufacturers can control complexity and security.
With edge computing, the data and the computing resources are located close to the machines. This enables users to gain real-time information about the efficiency of different aspects of their industrial automation system. This means that they can access intelligence within the machine, which in turn enables deep analysis to be carried out.
Manufacturing companies are increasingly recognising that AI can make a major contribution to their profitability by increasing their OEE, which in turn will lead to greater productivity and lower costs.
So, Cloud or edge computing?
In a traditional machine control environment, it has been impossible to programme a machine to recognise micro-second skill patterns in the local data that might be entering it. However, advances in technology mean that you can have machine control equipment that will process that data and recognise patterns within it.
Despite cloud and edge computing having some clear differentiations in the world of manufacturing, one does not need to replace the other. Both cloud computing and edge computing can complement each other in many ways - it is perfectly possible for the two to co-exist. For example, computing can take place in the cloud and then be transferred to edge devices.
Both having their valued roles to play, the question manufacturers are asking – for a factory that is beginning to use AI – which is the most effective solution? Edge computing – it seems clear that when using AI in the production line, one solution has the edge.
About the author:
Tim Foreman is the European R&D Manager at Omron, where he started back in 1993 as a Software Engineer. Tim has a PhD and MSC in Physics from Utrecht University and has held a variety of positions from Project Leader, and Group Leader to Development Manager. In 2007 he was appointed to his current position as European R&D Manager.
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