Digital Twin Sensor Data Transformation for Structural Monitoring
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Solution Overview
Problem
Current monitoring approaches for physical assets are limited by the number, type, and location of physical sensor installations, leading to high costs and inadequate real-time analysis, which becomes inadequate when conditions change or complex scenarios develop, such as during the deterioration of a structure.
Innovation Solution
A system and method for real-time structural analysis using a digital twin, where sensor data from physical assets is transformed into virtual information and applied to a virtual asset, allowing for reduced sensor hardware costs and the use of virtual sensors at any location, including inaccessible areas, with an enhanced rainflow counting algorithm for damage detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical sensors are installed on the physical asset to monitor structure, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the physical asset that replicates its structural behavior. Instead of installing numerous physical sensors throughout the structure, a minimal set of sensors feeds data to the digital twin, which then simulates and predicts structural conditions at any location. This copying approach maintains measurement precision while dramatically reducing device complexity and sensor installation requirements.
Solution Approach 2:
The digital twin acts as an intermediary between the physical sensors and the monitoring system. Rather than directly measuring conditions at multiple locations with physical sensors, the system uses sensors combined with the digital twin model to indirectly determine structural states. This intermediary approach enables comprehensive monitoring with minimal physical sensor installation.
2Measurement precision
If more physical sensors are installed to cover all locations, then measurement precision is improved, but loss of time for installation and maintenance increases
Solution Approach 1:
By creating a virtual replica of the entire structure with virtual sensors distributed throughout the digital twin, the system achieves comprehensive location coverage without physically installing sensors at every point. The virtual sensors are instantly available in the simulation model, eliminating the time-consuming process of physical sensor installation and maintenance at multiple locations.
Solution Approach 2:
The patent transitions from physical space to virtual/digital space for comprehensive monitoring. Instead of expanding physical sensor coverage in three-dimensional space, the system creates a parallel virtual model where sensors can be placed anywhere without physical constraints. This dimensional shift to virtual space enables complete coverage without the time penalties of physical installation.
3Reliability
If physical sensors are used to monitor all aspects of the asset, then reliability is improved, but loss of substance (sensor hardware resources) increases
Solution Approach 1:
The digital twin serves as a virtual substitute for extensive physical sensor networks. By replicating the asset in virtual space with computational models and virtual sensors, the system maintains reliable monitoring capabilities while eliminating the need for numerous physical sensors and their associated hardware resources, installation infrastructure, and maintenance activities.
Solution Approach 2:
The patent replaces the mechanical/physical sensor system with a computational/digital system. Instead of relying on physical sensors to directly measure all structural parameters, the system uses computational models in the digital twin that calculate structural behavior based on minimal sensor inputs. This substitution eliminates most physical sensor hardware while maintaining or improving monitoring reliability through simulation and prediction capabilities.
4Device complexity
If generic mathematical algorithms are used for digital conversion, then device complexity is reduced, but adaptability worsens when conditions change
Solution Approach 1:
The digital twin employs dynamic, adaptive algorithms that can adjust to changing asset conditions rather than relying on fixed generic mathematical models. The simulation model can incorporate updated parameters, material properties, and environmental conditions as they change over time, allowing the system to adapt to deterioration, load changes, or unexpected scenarios while maintaining manageable complexity through modular computational architecture.
Solution Approach 2:
The system allows for parameter changes in the digital twin model to reflect real-world condition changes. When the physical asset deteriorates or operating conditions change, the corresponding parameters in the virtual model can be updated, enabling the algorithm to adapt to new scenarios. This parameter flexibility provides versatility without requiring complete algorithm replacement, maintaining a balance between complexity and adaptability.
Data Source
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AI summary
Provided are devices and methods for monitoring a physical asset in real-time based on simulated data being transformed and applied to a virtual asset corresponding to the physical asset. In one example, the method includes receiving sensor data of a physical asset, the sensor data including position information associated with a location on the physical asset, transforming the position information into virtual position information and applying the virtual position information to a virtual asset corresponding to the physical asset, detecting movement information of a virtual structure of the virtual asset based on the transformed and applied virtual position information, and outputting information concerning the detected movement information for display to a display device.