Structural Anomaly Risk Ranking Using Multi-Inspection Digital Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for detecting and prioritizing anomalies in structures are inadequate, leading to insufficient budget allocation and increased risk of catastrophic failures due to unaddressed critical anomalies, while digital sensor data processing adds to inspection costs.
Innovation Solution
A method involving remote inspection devices, digital modeling, and algorithmic analysis to detect, quantify, and prioritize anomalies based on objective parameters, enabling a risk ranking and resource optimization for maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all detected anomalies are repaired immediately, then the reliability of structures is improved, but the cost of maintenance increases beyond available budgets
Solution Approach 1:
The patent transforms the static severity classification into a dynamic risk assessment by introducing temporal evolution parameters. The risk score is calculated based on how anomalies change over time between inspections, converting a budget allocation problem into a time-based priority ranking that optimizes limited resources while maintaining structure reliability
Solution Approach 2:
The patent performs preliminary risk assessment and prioritization before maintenance execution. By calculating risk scores based on anomaly evolution between inspections, the system pre-determines the optimal maintenance sequence, allowing operators to proactively allocate budgets to high-risk anomalies before they cause failures
2Ease of operation
If static severity classification is used to prioritize anomalies, then the ease of operation is improved, but the measurement precision of anomaly importance deteriorates
Solution Approach 1:
The patent transitions from static severity classification to dynamic risk assessment by incorporating temporal dimensions. The risk score evolves based on anomaly behavior between inspections, making the prioritization system adaptive and precise while remaining operationally simple through automated scoring
Solution Approach 2:
The patent implements feedback mechanisms by comparing anomaly states across multiple inspection time points. This feedback loop allows the system to learn from anomaly evolution patterns and continuously refine risk assessments, improving measurement precision while maintaining ease of operation through systematic data reuse
3Measurement precision
If digital sensor data is processed extensively to detect all anomalies, then the measurement precision is improved, but the cost of inspection increases
Solution Approach 1:
The patent applies partial action by focusing processing efforts on anomalies that show evolution between inspections. Rather than uniformly processing all detected anomalies, the system selectively intensifies analysis on changing anomalies, achieving high measurement precision for critical cases while reducing overall inspection costs
Solution Approach 2:
The patent performs preliminary filtering by comparing anomaly states between inspection time points before detailed analysis. This preliminary action identifies which anomalies require extensive processing, allowing the system to achieve high detection accuracy for evolving anomalies while avoiding unnecessary processing costs for stable anomalies
Data Source
AI summary
The invention relates to a method for the detection and treatment of anomalies in sets of structures associated with the same operator, fixed or mobile, such as wind turbines, or linear industrial infrastructures, which includes performing a first inspection of the set of structures, obtaining a digital model of each of them, processing them to detect anomalies, analyzing synergies between them, performing a second inspection for the same purposes as the first one, comparing the results of both, analyzing the possible evolution of the anomalies and determining the degree of risk of each structure in relation to the other structures of the set. This provides an overall picture of the situation of the set of structures in terms of their maintenance requirements, making it possible to prioritize spending for these purposes.