Statistical Load Weight Inference for Structural Health Monitoring
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Solution Overview
Problem
Structural health monitoring techniques face challenges in analyzing load responses from unknown weight objects without the need for costly and inconvenient controlled load testing, as existing methods require known load conditions to validate structural models accurately.
Innovation Solution
A method and system that measure load responses from a sample of traffic loading events using sensors, determine statistical parameters, and correlate them with known object weights to assign weights to unknown loads, allowing for the use of inferred 'known' loads for analysis, thereby eliminating the need for controlled load testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If controlled load testing is used to validate structural models accurately, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent creates a virtual copy of the controlled load testing process through computational simulation. Instead of physically applying controlled loads, the system uses statistical parameters from ambient traffic data to simulate controlled loading conditions in a virtual model, achieving validation accuracy without physical testing complexity
Solution Approach 2:
The patent replaces the mechanical controlled load testing system with a computational statistical analysis system. Statistical parameters extracted from ambient traffic loading events substitute for physical load application, transforming a mechanical testing process into an information-processing approach that maintains validation precision while eliminating testing infrastructure
2Reliability
If controlled load testing is conducted to ensure accurate structural analysis, then reliability is improved, but loss of time and productivity decrease
Solution Approach 1:
The patent performs preliminary statistical analysis on ambient traffic data collected during normal asset operation. By continuously gathering and analyzing loading event data in advance, the system prepares validation information without requiring dedicated testing time, making reliability assessment an ongoing process rather than a time-consuming event
Solution Approach 2:
The patent transforms model validation from a discrete, time-consuming testing event into a continuous process. Statistical parameters are continuously extracted from ongoing ambient traffic loading events, providing continuous validation feedback that maintains reliability without interrupting normal asset operation or requiring scheduled testing downtime
3Ease of operation
If ambient traffic loading events are used for model calibration, then ease of operation is improved, but measurement precision may worsen
Solution Approach 1:
The patent transforms the unknown weight parameter into a determinable statistical parameter through correlation analysis. By changing the approach from direct weight measurement to statistical parameter extraction and correlation with traffic data, the system maintains precision while simplifying operation - the weight becomes a calculated parameter rather than a direct measurement
Data Source
AI summary
A set of load responses of an asset for a sample of traffic loading events caused by objects of unknown weight is measured. At least one statistical parameter is determined from the set of load responses. A corresponding statistical parameter of known object weights loading the asset is determined. An object weight is assigned to a load response of the set of load responses based on correlation of the extracted statistical parameter to the corresponding statistical parameter.