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  • Industrial IoT Automation

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    Nadine Cranenburgh

    Introduction

    The IoT era has been marked by an exponential increase in hardware and software capabilities. This has meant that industrial automation control systems have moved from stand-alone, discrete, relay-based automation systems towards multi-processor systems, edge- or cloud-connected systems as shown below.

    609145811_evolutionofautomationinIoT.PNG.269ebf4572bc82a72660622f184e16a4.PNG

    Diagram courtesy of Chris Vains, Siemens Australia NZ

    This transformation in automation systems has been mirrored in the data formats, which have moved from paper charts and manual reports to big data applications which can collect insights from multiple sites around the globe and enable predictive analysis and decision making.

    1643158642_datachanges.PNG.aaa5f7db805a373e348a42601d725514.PNG

    Diagram courtesy of Chris Vains, Siemens Australia NZ

    A major shift in focus has been the increasing trend to use industrial data to improve processes in the future as well as find out why things went wrong in the past. One example is the use of ‘prescription’ which is the process of changing the use of assets to extend their life or improve processes when data analytics have predicted a failure is imminent.

    Industrial IoT (IIoT) requirements and data model

    IoT solutions for industry have specific requirements. As well as needing to be reliable and robust enough to survive in an industrial environment, they need to be scalable and meet industrial cybersecurity standards. Interoperability with legacy systems, which may be 10 to 20 years old, is also key, and industrial clients require the ability to customise solutions to meet their specific requirements. Solutions need to be commercially available in the long term to provide continuous business models to clients.

    A general example of an IoT data model for a manufacturing application is shown below.

     

    1721275034_IoTdatamodel.PNG.4e76626642810e6dd8ca03226a4eb0bc.PNG

    Diagram courtesy of Chris Vains, Siemens Australia NZ

    In industrial applications, the cost of monitoring reliability of equipment to minimise production downtime needs to be balanced against the cost of the parts, as it might be cheaper to run to failure rather than monitor depending on the cost of downtime. The diagram below shows the kinds of input data and analysis that might be implemented for a packaging production line application.

    1938629352_exampleforpackagingproductionline.PNG.f54a2ae307e9991cd00e7815614da49a.PNG

    Diagram courtesy of Chris Vains, Siemens Australia NZ

    Platform as a service

    One cloud-computing tool for the IIoT is ‘platform as a service’ (PaaS). This is an IoT operating system that connects things and collects data. API interfaces allow other service providers to connect and offer additional functionality such as data analytics. It can also be used for application development, testing and deployment. Examples include Siemen’s MindSphere, Honeywell Sentience and GE Predix.

    Staged approach

    Rolling out IoT-based automation in industry generally follows a staged approach. For example, operations might start by implementing a data-driven approach to improve transparency and asset management and use data to automate a standalone process. As the IoT solution merges with the company’s systems and data collection and integration becomes more mature, it will start to drive proactive automation of processes.

    Industry can then progress to introducing digital ‘twins’ for products, production and performance to feed data on potential changes into offline models before implementing them in the production plant. The final stage is using tools such as augmented reality and AI to gain high-level insights from data that can drive innovation and system optimisation.

    This concept is illustrated in the diagram below.

    279505799_stagedapproach.PNG.c8f11db11f6c50bd2df771b638d1cebd.PNG

    Diagram courtesy of Chris Vains, Siemens Australia NZ

    Future of IIoT Automation

    As technology continues to advance, future applications of IoT automation could include:

    • distributed intelligence
    • edge control eg. analytics and model-based control
    • local optimisation eg. line monitoring and control devices to optimise performance of a production line
    • connectivity to data outside of the reach of today’s control systems eg. environmental or weather data
    • real-time simulation eg. data feedback for process optimisation or testing measures to improve performance.

    The diagram below shows a possible future data model for IIoT solutions, where edge control is the gateway for information flowing to and from the cloud to drive decision making.

    1858102486_Futuredatamodel.PNG.f097aca2955efbc6a29513575440895c.PNG

    Diagram courtesy of Chris Vains, Siemens Australia NZ

    Sources:

    The content on this page was primarily sourced from the following:

    Edited by Nadine Cranenburgh



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