Digital Twin

The Digital Twin concept in Industry 4.0 come into practice when we addressed the question like: “Do we need to adjust the product?” and then we think “Let’s look at the original specs, at the drawings, at snapshots, at the BOM, at simulation results, at VR iterations, at the real performance parameters” – We simply say then: “Let’s look at the digital twin!”
Digital Twin is a mirroring (or twinning) of what exists in the real world and what exists in the virtual world. It contains all the informational sets of the physical ‘thing’ meaning its cross-discipline – not just a mechanical / geometric representation, but also including the electronics, wiring, software, firmware, etc. not just the CAD model.
Digital Twins are currently used in the context of monitoring, simulation and predictive maintenance which are all incredibly valuable and potentially transformative in their own right, however, there would seem to be much more to it. As products of all types move to include connectivity, sensors and intelligence we can’t just think about the data streaming back from the field.
Without accurate “Context” – Digital Twin – time series data generated during production and ongoing operation is difficult or even impossible to understand and analyze. In addition, the ability to interpret and act upon these data often require traceability to prior information from related revisions – Digital Thread. To complicate matters further as artificial intelligence / cognitive computing is introduced the necessity for the Digital Twin becomes even greater.

Predictive Maintenance

Since new sensors and IoT devices can be integrated in production processes and operations, the availability of data increases drastically. AI-based algorithms are capable of recognizing errors and differentiating the noise from the important information to predict breakdowns and guide future decisions.
“Predictive maintenance strategies are based on the combination of traditional condition monitoring enhanced with analytics algorithms, thus enabling the prediction of machine failures before they occur.” https://iot-analytics.com