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Catch hereEDA


across the production environment and here we get into the realm of Big Data Analysis. AA and AI technologies enable different machine states to be recorded and analysed in real time to recognise the current machine status, detect potential faults on the horizon and immediately offer recommendations for actions to the machine operator or autonomously initiate remedial actions. Reaping the maximum benefit from this development 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 floor can be connected to Edge Computing platforms for efficient processing for example and on to MIS/ MES and ERP systems, then the full benefits of AA and AI are realised. This level of integration enables a far greater range of KPIs to be analysed and so can be used to drive improvements in overall equipment effectiveness (OEE). What we see then, with control systems built around AA and AI technologies, are machines that are self-learning and self-optimising. The importance of Artificial Intelligence to the machine control market cannot be overstated. In addition to developing products that incorporate a connection to cloud based AI as a service, IBM’s Watson for example, Mitsubishi Electric for one has developed several in-house AI algorithms and services and is positioning its developments in AI technologies under its own brand to reflect its growing importance.


Processing at the Edge


Managing the crossover between Information Technology (IT) and Operational Technology (OT) is the next major challenge. The successful merge of these worlds 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.


It is worth it though – the most recent technology developments are based on edge computing, which provides the answer by bridging the gap between IT systems and plant level automation. Edge devices can collect and analyse data from neighbouring automation systems and make decisions in real time to influence the production process.


Using this technology effectively can provide a huge competitive advantage. It also creates new challenges: from system compatibility to data security. On the other hand, edge computing systems can be easily interconnected with cloud


www.cieonline.co.uk


services to provide scalable data storage and management solutions. In this way users have all the benefits of IT systems, without storage issues or being influenced by potential threats.


Looking after your assets Against the backdrop of a desire to increase OEE by means of digitalisation, there is a high demand for analysis of extracted data (data mining) from production. The condition and operating profile of plant automation devices and machines for example like a production robot’s components such as servo drives can be recorded. This provides valuable information for example the status of wear parts and any contamination. The resulting database information then enables predictive maintenance strategies with a significant saving potential in maintenance costs. To improve these strategies further, edge computing technology [as described earlier] is being used to leverage the value of manufacturer’s data using advanced analytic algorithms executed on the Edge of the shop floor. Another important category of process data is the one that is used for traceability and consumer information, especially in the food sector. This can be employed, for example, to prove compliance with the cold chain or to attach origin information to food packaging that can be called up 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.


Predicting the future AI is certainly playing a key role in manufacturing, moving from vision recognition to skill learning and predictive maintenance for failure prevention, however it has further scope for providing operational benefits and efficiencies. When detecting impending faults and informing operators how to fix problems for example we see AI again coming to the fore. AI is being used to increase the effectiveness 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 efficiency.


gb3a.mitsubishielectric.com Components in Electronics July/August 2020 35


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