DIGITAL TWIN IMPLEMENTATION
Published in
3 min readFeb 23, 2021
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𝟳 𝗦𝘁𝗲𝗽𝘀 𝘁𝗼 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗮 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗧𝘄𝗶𝗻
Industry 4.0 Maturity Model
■ From our simple conception, we can perceive that Digital Twin, not only represents a virtual structure of an asset but also bear a resemblance to its virtual behavior.
■ The description of Digital Twin has been nicely evaluated over the last few years, yet it seems when it’s time to pilot projects, it is neglected.
■ Organizations need to strive for the exact, individual configuration of the product, process, or asset, yet the solutions they are implementing do no such thing.
■ By working out these twists, everything else including performance monitoring, IIoT, predictive analytics, and operational simulation will fall into place, creating remarkable value for the organization.
■ Manufacturers need to change their philosophy and build the foundation of each asset and system they want to manage.
■ Due to the many uses for Digital Twin across the product lifecycle, they also need a flexible/dynamic data model ingrained in the technology they choose.
■ Organizations should start their Digital Twin strategy by capturing and managing the actual physical configuration of the asset and each and every event of their process and operations.
■ Only a dynamic data model will support the evolving needs and various configurations of Digital Twins, based on the use case.
■ Organizations need to know what assets they have within the systems they are operating and maintaining.
■ The model number of the asset, serial number, maintenance history, and operating history of that asset should be known before using the data.
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𝗔𝘁 𝘁𝗵𝗶𝘀 𝗷𝘂𝗻𝗰𝘁𝘂𝗿𝗲, 𝗵𝗲𝗿𝗲 𝗶𝘀 𝗺𝘆 𝗮𝘁𝘁𝗲𝗺𝗽𝘁, 𝘁𝗼 𝗰𝗵𝗮𝗿𝗮𝗰𝘁𝗲𝗿𝗶𝘇𝗲 𝘁𝗵𝗲 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗼𝗳 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗧𝘄𝗶𝗻 𝗶𝗻 𝟳 𝘀𝘁𝗲𝗽𝘀.
𝗦𝘁𝗲𝗽 𝟭 — 𝗣𝗹𝗮𝗻
■ Plan and Define the product characteristics for Digital Twin.
𝗦𝘁𝗲𝗽 𝟮 — 𝗕𝘂𝗶𝗹𝗱
■ Connect the sensors to capture the attributes of 5 M — Man, Material, Machine, Method, and Maintainance.
𝗦𝘁𝗲𝗽 𝟯 — 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗲
■ Communicate the sensor data to Cloud.
𝗦𝘁𝗲𝗽 𝟰 — 𝗖𝗼𝗺𝗯𝗶𝗻𝗲
■ Accumulate the gathered sensor data.
𝗦𝘁𝗲𝗽 𝟱 — 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀
■ Cultivate the Big Data Analytics for visualization to analysis.
𝗦𝘁𝗲𝗽 𝟲 — 𝗣𝗿𝗲𝗱𝗶𝗰𝘁
■ Use AI ML to develop a predictive model. This is the realization phase of Digital Twin and we can simulate the product or production behavior under certain circumstances.
𝗦𝘁𝗲𝗽 𝟳 — 𝗔𝗰𝘁
■ Utilize the outcome of a predictive model, to take action, to optimize the Product Characteristics and production process.
■ We can further mature and fine-tune this process by adding more data sources and as well as learning from iterative Digital Twin life cycles.
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Academy ti4 | www.ti4.org
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