ARTIFICIAL INTELLIGENCE
Getting on board with AI technology It is becoming a reality that technologies such as artificial intelligence (AI) are starting to change the traditional role of the control engineer. Suzanne Gill finds out more about the benefits it can offer engineers as well as the barriers to its adoption in the industrial environment.
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ccording to Jos Martin, senior engineering manager at MathWorks, the biggest impact of AI on control engineering in the coming years will be on the workers themselves. He said: “As demand for data science skills grows and the tech skills gap widens, everyday engineers and scientists, as well as data scientists, will be expected to fill the gap, undergoing training on how to design and deploy machine learning systems to become ‘citizen data scientists’. To be able to make the most of AI in their work, engineering professionals will need to possess skills such as the ability to deal with large datasets, and to build and train AI models and understand how to use new development tools and software. Companies need to support their workers to upskill and must be willing to invest in adequate training to make this a reality.” Hartmut Pütz, president Factory
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February 2020
Automation EMEA at Mitsubishi Electric Europe, agrees that AI will affect the control engineering role. He said: “Control engineers will need to change their daily task list. Their role will start to include much more data analysis activities. When users start to implement more self-learning and self-optimising technology in processes a big part of the control engineering objectives will change and this will mean that engineering skillsets will also need to change. I believe that the job profile will become more aligned with that software engineering and data engineering. “In around 10-15 years it is very likely that process optimisation will be handled entirely by AI technologies and the ability to programme PLCs will become much less important. Even today we are seeing PLC programs being generated automatically by higher level systems in the simulation space and then downloaded into the PLC.”
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Improving efficiencies AI algorithms are starting to improve the efficiency of the entire factory production line, reducing energy consumption and waste, enabling organisations to meet important corporate social responsibility targets as well as deliver cost-savings. Traditionally, to achieve good AI accuracy levels and easy training of models, the use of high-performance computing systems such as GPUs, clusters and data centres that use 32-bit floating-point math, have been vital. However, developments in software tools now mean that AI inference models, which use a range of fixedpoint math, can enable engineers to capitalise on devices such as electronic control units and other embedded industrial applications that run on lower power. AI is helping to improve the accuracy of predictive maintenance applications – such as those for predicting the remaining useful life for an industrial site pump. However, one of the biggest barriers to its adoption in the industrial space is having enough high-quality data to properly train AI models. “Lots of failure data is needed to ensure the AI model is accurate, but it is expensive and inefficient to create data from real, physical equipment. Fortunately, improvements in software now make it easier to recreate data from critical failure conditions and anomalies by generating simulations representing failure behaviour and synthesising it to train a model,” said Martin. “We are seeing AI being used to transform design in everything from Control Engineering Europe
