Moving forward with AI in industrial manufacturing – What areas should you consider?
Author : Dr Tim Foreman, European R & D Manager, Omron Industrial Automation
17 October 2019
Everyday manufacturers are becoming increasingly dynamic with innovative enhancements that are providing a multitude of societal benefits. With the increase of automation solutions, more powerful hardware and software, and the power of innovation, this is leading machines to become increasingly more efficient which results in constant benefits throughout the value chain.
The reliance between man and machine is on the increase, both from a functional and future innovation stand point.
The AI relationship test – The Cloud vs the Edge
Cloud computing and Edge computing – two key technological advancements yet understanding which is most effective when enhancing machine development is a frequent issue businesses face.
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 for businesses – and in particular, 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 to 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: Do you know what you are trying to fix?
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: Are you using your data to the best of your ability?
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.
In conclusion, cloud or edge?
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.
The key takeaway that manufacturers should consider is that you do not need to select one or the other – although there are obvious differences between both cloud and edge computing. To provide an example, edge devices can consume computing that takes place in the cloud. Researching what is the correct option is vital, nevertheless, the ability for both options to work in harmony, complementing each other and co-existing is perfectly possible.
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