Digital Twin Maturation Tracking for Asset Update Relevance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
As digital twins mature over time, they deviate from their original state, making it challenging for manufacturers and sellers to determine whether proposed changes, such as maintenance or recalls, are still relevant to the current state of the associated physical asset, leading to potential unnecessary updates and costs.
Innovation Solution
A computer-implemented method that compares the original digital twin of a physical asset to its mature state, determining the relevance of proposed changes and allowing only applicable updates to be applied, while providing incentives for owners who have already performed relevant changes, thereby reducing costs and maintaining asset performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manufacturers and sellers apply proposed changes to all digital twins, then they ensure comprehensive coverage and reliability, but they incur unnecessary costs and perform unnecessary updates on assets that have already been modified
Solution Approach 1:
The system performs preliminary comparison between the original digital twin and the mature digital twin before applying any updates. By advance identifying deviations and determining relevance of proposed changes to the current asset state, the system prevents unnecessary updates from being applied, thus avoiding wasted resources while maintaining reliable update coverage for relevant assets
Solution Approach 2:
The system implements a feedback mechanism where the results of comparing original and mature digital twins inform subsequent update decisions. The comparison results feed back into the update application process, enabling the system to dynamically adjust which updates are applied based on the actual deviation between original and current asset states
2Loss of energy
If manufacturers track and compare original digital twins with mature digital twins to determine relevance, then they reduce unnecessary updates and costs, but they increase system complexity and processing requirements
Solution Approach 1:
The system extracts only the essential deviation information between original and mature digital twins that is necessary for determining update relevance. By taking out only the critical comparison data needed for decision-making rather than analyzing all possible parameters, the system reduces unnecessary updates and costs while avoiding excessive complexity in the comparison process
Solution Approach 2:
The system applies different levels of comparison and analysis to different aspects of the digital twin based on their relevance to update decisions. Rather than uniformly analyzing all parameters with equal depth, the system focuses computational resources on the specific local qualities and parameters that directly impact update relevance determination
Data Source
AI summary
Track transformations of mature digital twins from time of creation to decommissioning of the physical asset. As physical assets evolve, changes, repairs, maintenance and services relevant to the physical assets originally provided, can be applied or deemed inapplicable in the current state. Mature digital twins are leveraged to identify proposed changes to physical assets and compare original digital twins of the base asset to determine whether deviations over time render the proposed changes irrelevant. Irrelevant changes, due to deviations from the original digital twin, are further analyzed to determine whether the irrelevancy is due to a previous action performed on the physical asset and update to the mature digital twin. Previous updates to physical assets, reflected in the mature digital twin, can be eligible for rebates and incentives where the asset owners performed actions that render proposed updates irrelevant, while relevant changes are approved and permeated to mature digital twins.