Q: What is an Industrial Digital Twin?
An industrial Digital Twin is a virtual representation of an industrial part or machine that incorporates simulations and models with sensor field data to optimize design and create predictive maintenance regimes. The Industrial Internet of Things (IIoT) has created an ecosystem of smart machines, machine components, sensors and monitors—all connected to the Internet and pointed at a data center of some kind. The goal is for machines to talk to each other, communicate with humans and provide data that can be utilized for data-driven strategic business decisions.
Commercial digital twins are just now becoming possible with hyperscale data architecture and better collection of field data. The digital twin of any physical machine is continuously updated with real-time telemetry data from sensors, giving engineers insights they didn’t have before.
Q: What is the Business Value of an Industrial Digital Twin?
Digital twins have multiple applications at each link in the customer and manufacturing chain. So, its’ design and features usually depend on what part of the enterprise is driving the business need.
Builders of large power plants or factories, for example, might want a rapid proposal generation system in place that optimizes a number of different performance and economic models. The digital twin of that configured facility might factor in external data such as location, weather, and topography. Most importantly, that optimized configuration can be run in a few hours instead of the days and weeks it takes engineers to do this manually. Customers can use such a tool to choose the optimal model given budgetary, location, and other constraints. Peaxy calls this a Multifaceted Digital Twin because it orchestrates both performance and economic models to get a result.
Another type of digital twin allows engineers to monitor a particular machine’s performance, or even a small gear within that machine. The twin can provide alerts that identify discrepancies between modeled and real-time behavior (e.g. gearbox vibrational patterns). This data can identify potential breakdowns, useful for creating a Condition-Based Maintenance regime (CBM). CBM monitors the actual condition of an asset, compares it to simulations and modeled behavior, and then creates an optimized maintenance schedule that saves resources and time.
Digital twins are also useful in design optimization. Once you are closely monitoring an asset’s behavior in the field, discrepancies between simulated and real-time data expose small design flaws and point the way to maximum efficiency. Fixing a design flaw or making even a small enhancement to the design can save incredible amounts of money and time if they are found before the next fleet production cycle begins.
Q: Any Other Benefits of a Digital Manufacturing Twin?
Once the digital twin is created on a robust data architecture, collaborative work between disparate engineering groups becomes a lot easier. Unified access allows organizations to share data contained in the digital twin across multiple teams and departments, within and outside the organization.
The digital twin will bolster human safety in industrial settings. Remote monitoring and anomaly detection includes setting alerts to highlight malfunctioning parts, reducing hazards for inspectors.