Relative Response Ratio Load Localization on Digital Twins
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing real-time asset monitoring systems for load identification on structures are computationally expensive, require numerous sensors, and are sensitive to finite element analysis accuracy, making them impractical for accurate load location and magnitude detection, especially in complex structures.
Innovation Solution
A system and method using relative response ratio (R3) values to identify load location and magnitude by attaching sensors to a structure, generating a digital twin, and calculating R3 values based on simulated responses, allowing for real-time monitoring with fewer sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing inverse problem solutions are used for load identification, then load location and magnitude can be identified, but the computational cost and time required increase significantly
Solution Approach 1:
The patent pre-calculates and stores the influence matrix for all possible load locations and sensor configurations before actual monitoring. This influence matrix contains the relationship between loads and sensor responses, allowing rapid inversion during real-time monitoring without performing computationally intensive inverse problems repeatedly.
Solution Approach 2:
The patent creates a digital twin model that replicates the structural behavior through a simplified mathematical representation. This digital twin uses pre-computed influence matrices that capture the essential dynamics, allowing fast simulation and identification without requiring complex real-time FEA calculations.
2Measurement precision
If a large number of sensors are deployed to improve measurement accuracy, then load identification precision improves, but the system complexity and cost increase
Solution Approach 1:
The patent transforms the identification approach from using multiple sensors with complex spatial distribution to using a reduced set of sensors with optimized positioning. The influence matrix method allows accurate load identification with fewer sensors by changing the parameter space from sensor count to sensor strategic placement.
Solution Approach 2:
The patent designs the sensor system to serve multiple functions: the same sensors used for basic structural monitoring also enable load location identification, magnitude calculation, and dynamic characteristic extraction. This multi-functionality reduces the need for dedicated load identification sensors.
3Measurement precision
If model-based methods are used for load identification, then theoretical accuracy improves, but sensitivity to FEA model accuracy and operational noise increases
Solution Approach 1:
The patent implements a feedback mechanism where sensor measurements are continuously compared with predicted responses from the influence matrix. The system uses this feedback to refine the load identification results and compensate for model inaccuracies and operational noise through iterative correction.
Solution Approach 2:
The patent pre-calculates the influence matrix using comprehensive FEA models that capture various operational conditions and noise scenarios. This pre-computed matrix acts as a cushion against real-time model inaccuracies by incorporating worst-case scenarios and uncertainties during the pre-processing stage.
Data Source
AI summary
The present disclosure relates to a system (100) and a method (200) for load identification using relative response ratio (R3) values. The system (100) for identifying load location using R3 values includes two or more sensors (130) attached to a structure (120) to measure a structural response value when a load is applied to said structure (120). The system (100) further includes one or more processors (102) configured to: receive a structural response value measured by each of the two or more sensor(s); determine an R3 value between each pair of the sensors; from a database (110), retrieve simulated R3 values associated with each pair of sensing areas (325) on a digital twin (310) of the structure (120), and determine the location of the load being applied to the structure (120) based on the location associated with the simulated R3 value that is substantially equivalent to the determined R3 value.