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INDUSTRY 4.0/IIOT
HOPE, HYPE AND FOMO AI:
When I asked why they wanted to get into AI, the answer, more often than not, was either ‘because my boss asked me to’, ‘because we have a lot of data’ or ‘because it seems like an interesting area’. None of these were particularly solid reasons and certainly didn’t justify the hefty investment that AI applications would have required at that time. That is changing, and hope, rather than
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hype or ‘FOMO’, is the driver. Customers don’t ask whether we can help them with AI anymore. They ask us whether we can help with predictive maintenance, quality control or process optimisation. They come to us with a problem that they want us to help solve. The adoption of AI is no longer a motive or an aim in itself. Instead, AI has become an ‘enabler’. Whatever the industry - whether food &
drink, pharmaceutical, electronics or automotive - the end goal is the same: producing high quality, defect-free products at a lower cost, using less energy and less labour. And AI can be one of a suite of solutions for achieving those objectives.
Past, present & future Just as attitudes have evolved, so has the technology. Although AI has existed as a concept since 1957, early applications were unfeasibly expensive and slow: it took a month to obtain the results of a simple calculation due to processing power limitations. Thanks to advancements in mobile technology, computer storage and processing speeds, today, calculations can be carried out in milliseconds and the cost has fallen considerably. Although tech giants like Amazon and
40 NOVEMBER 2022 | PROCESS & CONTROL
en years ago when we received enquiries about AI-based projects, they were mainly driven by hype or FOMO.
Tim Foreman, research and development manager, OMRON, looks at how AI has become an enabler for predictive maintenance, quality control and process optimisation
Google have been using AI for some time, AI is still in its infancy in an industrial or factory floor context. I would liken its lifecycle stage to that of robotics 15 years ago, when you needed a maths degree to control a six-axis robot. To implement AI-based systems, you still need experts; you need to understand what you are doing and it only makes sense in niche applications where the cost of entry can be justified by the benefit. It is also important to remember that AI is
not a panacea. As machine builders, data scientists and engineers, we can be guilty of automatically defaulting to tech for the answers, when the more straightforward solution is far simpler and less sophisticated.
Take for example, a piece of conveyor that
is broken and bent. That is an engineering problem that can be identified and resolved using a traditional mechanical solution. It is the less obvious, intermittent issues – for instance manifesting in micro-stoppages - where AI can add value.
Perform a ‘sanity check’ Here’s a real life example: we were called in to help an automotive customer who was having problems with micro-stoppages. After performing a data scan, we carried out a ‘sanity check’. This involved connecting probes to the machine to create pictures of the signals that were being generated to
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