40 40th Anniversary thAnniver yras
treated as streaming data. Concurrently, the speed of data analysis is playing an increasingly important role. As such, updating the dashboards once a day or every hour is simply not enough. An operator needs to be informed about potential problems immediately to avoid difficulties and downtime. Ideally, the machine should therefore be notified in real time so that it can automatically correct itself within the same product cycle. In addition, data may be corrupted due to a problem in the sensor or other device, it might go missing or it could be recorded in an outdated manner. Because these scenarios can seriously compromise analysis and lead to false conclusions, data scientists must continually check the “Veracity” of the data – a fourth “V”. Industrial Data Science is a relatively new discipline, which is why there is no broadly valid approach that is suitable for every company. Every solution and application requires customised data analysis and modelling to achieve the best possible result. However, a standard approach is useful. The CRISP-DM model, (Cross-Industry Standard Process for Data Mining) is the most commonly adapted basis. OMRON simplified and tailored CRISP-DM into a new approach. The four steps of this approach are preparation, analysis and application
INDUSTRY 4.0/IIOT
development, evaluation and maintenance. More information about these phases can be found in the box on page 26.
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A data-driven solution does not always have to include complex machine learning models or artificial intelligence. Sometimes, effective data processing to provide the right information at the right time in the right way can be enough. An illustrative example of such a data science project can be found in the whitepaper "Data Science Services by Omron – How to get the full value from your factory floor data", which is available for free download. The project was carried out at the Omron Manufacturing of the Netherlands
(OMN) factory on surface-mount technology (SMT) lines where electronic components are mounted and soldered onto PCBs Developing the potential of Big Data in your
own production environment is no small feat, but it’s worth doing. In today’s manufacturing environment, it’s not enough to just collect data and build a few graphs. Instead, filtering out production-relevant information from the data and presenting it to the appropriate audience in the right way is vital, transforming data into useful information.
Omron
industrial.omron.co.uk
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