FEATURE Industry 4.0 & Smart Factories y
AI-enabled smart factories
With the advent of AI, the smart factory is making production efficiency improvement decisions by itself, says Mitsubishi Electric Europe, Industrial Automation
T
he fi rst stage of becoming a smart factory is digital network communication. Having the right data infrastructure allows
companies to create, move and use data effi ciently, with which come homogeneous control and fast responsive manufacturing. State-of-the-art technology such as
artifi cial intelligence (AI) will help get the most from a smart factory, improving the performance and effi ciency of equipment and human resources.
AI in the middle AI is still a relatively young technology, but one that’s expected to make a massive impact on the industrial environment in the near future. Many companies are already integrating it into factory automation equipment. Applying AI principles to individual machine processes can already help reduce auto-adjustment times, synchronise increasingly-complex systems and suggest solutions to operators. It can even make autonomous decisions in real time, based on measured and programmed data, further optimising the process. Making reliable predictions based on experience, evidence and guidelines is a fundamental function of human intelligence. AI is no diff erent, as it contributes toward more eff ective predictive maintenance, monitoring the condition of components to enable replacement before damage occurs, and hence preventing unplanned downtime. Combining Advanced Analytics (AA) and AI will further extend traditional machine control architectures with more advanced data processing, learning and decision- making processes, to deliver increased productivity, effi ciency, reliability and accuracy, but also open up new possibilities for machine control.
Reaping the maximum benefi t from such developments will depend on control systems that not only embed these technologies but which also provide higher levels of connectivity. If the full spectrum of data sources on the plant fl oor can be connected to edge computing platforms for effi cient processing, for example, and on to MIS/ MES and ERP systems, then the full benefi ts of AA and AI are realised. This level of integration enables a far greater range of
20 September 2020 | Automation
AI has already been applied in the food industry for process improvements
KPIs to be analysed and so can be used to drive improvements in overall equipment eff ectiveness (OEE). With control systems built around AA and AI technologies, machines become self-learning and self-optimising.
Processing at the edge Managing the crossover between Information Technology (IT) and Operational Technology (OT) is the next major challenge. Their successful merging needs to address the skills gap that has traditionally existed between FA experts and IT departments. Historically, the OT layer is managed by automation engineers who do not necessarily have extensive IT skills, while programmers and IT system architects may not completely understand the automation world. But, since most recent technology developments are based on edge computing, gaps between IT systems and plant-level automation can be bridged. Edge devices can collect and analyse data from neighbouring automation systems and make decisions to infl uence the production process. Edge computing systems can also be easily interconnected with cloud services to provide scaleable data storage and management solutions. In this way, users have all the benefi ts of IT systems, without storage issues or being infl uenced by potential threats.
Looking after assets Against the backdrop of aiming to increase OEE by means of digitalisation, there is high demand for analysis of extracted data (data mining) from production. The condition and operating profi le of plant automation devices and machines, for example, like a production robot’s components such as servo drives can be recorded to provide valuable information about its status of wear parts. The resulting information then enables predictive maintenance strategies with a signifi cant saving potential in costs. To improve these strategies further, edge computing technology is being used to leverage the value of the manufacturer’s data using advanced analytic algorithms executed on the edge of the shop fl oor. Process data is also used for traceability and consumer information, especially in the food sector. This can, for example, prove compliance with the cold chain or attach origin information to food packaging to be accessed via a QR code. Data collected from PLCs, controls and drives centrally and processed locally using edge computing reduces the bill for storage space in the cloud, in addition to delivering many other advantages for faster production control and monitoring.
Into the future AI is already playing a key role in manufacturing, moving from vision recognition to skill learning, and predictive maintenance for failure prevention; however, it can also provide operational benefi ts and effi ciencies.
AI is being used to increase the
eff ectiveness of predictive maintenance for plant automation assets. Cloud-based solutions using AI platforms analyse operational data and can optimise maintenance regimes based on actual usage and wear characteristics. Predictive maintenance for plant automation assets can, of course, reduce operational costs, increase asset productivity and improve process effi ciency.
CONTACT:
Mitsubishi Electric Europe B.V.
gb3a.mitsubishielectric.com
automationmagazine.co.uk
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