The digital twin is a powerful tool for the efficient control of production systems and the optimization of maintenance processes. More and more companies are switching from conventional CAD models to digital twins when modeling production systems, because this form of virtual representation of real plant components and processes creates a detailed, complex image that has a high forecasting capability due to the quantity and quality of the data that can be integrated. As a result, optimum maintenance cycles lead to less wear and tear, shorter system downtimes and lower energy costs.
Digital twins: virtual representation for monitoring and optimizing physical systems
A digital twin is a virtual representation of a real system: a digital model that replicates the physical characteristics, behavior and performance of machines and systems. The connection between the digital twin and the physical objects is established via production data, for example from a process control system and other application-specific data sources. An illustrative example is the creation of a digital twin of a pump system. The traditional approach was to import CAD models from the manufacturer into simulation programs to generate the mechanical model. Hydraulic and electrical elements were then added to model the dynamic system behavior. This type of modeling is too complex for most applications, meaning that the effort and costs are disproportionately higher than benefits.
Digital twins, on the other hand, model the system behavior as a virtual representation. They use system data, measurement data from sensors and link these by applying artificial intelligence, such as machine learning or neural networks. This results in models that depict very complex properties and whose parameters can be changed for different forecasting scenarios without great effort.
As a minimum requirement, a digital twin should be able to access a set of master data: Basic information such as dimensions, manufacturer, technical specifications, spare parts lists and maintenance requirements are stored in a database and integrated into the model. Correct master data integration means that the digital twin can already perform initial functions, such as determining information on spare parts, adapting maintenance plans or providing details on repair activities.
How algorithms behind the digital twin optimize the performance of real systems
The forecasting ability of the digital twin can be specifically trained. For this purpose, actual data from various sources, i.e. from the process control system, maintenance planning, production planning, etc., is analyzed. is used to create a data model that allows the prediction of certain scenarios, such as a changed inspection interval or a wear-friendly operation of the system. Despite high computing power and storage capacities, statistical processing should be carried out to avoid the production of artifacts.
Digital twins give companies the opportunity to make decisions based on data that improve system performance, as a concrete example. A chemical production plant uses digital twins to monitor the pump system. By analyzing operating data such as pressure, flow rate and vibration, the algorithm can predict when a pump is likely to fail. Based on these predictions, the maintenance team can carry out targeted maintenance measures to minimize unplanned downtimes. The simulation of scenarios is another advantage that makes it possible to predict the effects of the decisions made and thus minimize risks. For example, it can also be used to determine optimum stock levels for spare parts. By constantly analyzing data, potential problems are identified earlier, which leads to higher system availability.
The bridge to predictive maintenance: the forecasting capability of the digital twin
For the digital twin to develop its full functionality, it requires measurement data collected by sensors on the condition and performance of the production facilities. The informative value of the analyses and forecasts produced by the digital twin increases with the quality of the available data. One application example is pumps that transport cooling water from the cooling towers to the consumers. Sensors on the pumps provide data on the pump performance or the flow rate, the speed and torque, the temperature of the pump motor and the energy consumption. In addition to the data mentioned, meteorological data such as outside temperature and humidity are also recorded, as these also have an influence on the cooling water temperature. This data can be used to train a neural network to predict the behavior of the pump system for other measurement parameters as well.
By continuously monitoring and analyzing oscillation and vibration data, the digital twin can monitor the condition of the system in real time and make precise predictions about the future performance of the system. Based on this data, companies are able to plan and carry out their maintenance activities in a time- and cost-optimized manner.
Digital twin: successfully launching into the future with ConMoto for planning and implementation for optimal use
A successful introduction and implementation of the digital twin requires planning and goal orientation. The new technologies and methods must be specifically coordinated and combined with existing process and system knowledge as well as classic change and implementation expertise. ConMoto supports you in defining the requirements for the design of the digital twin.
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