Infrastructure Component Optimization via Risk-Based Segmentation
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Solution Overview
Problem
The computational complexity of prioritizing and optimizing the replacement and upgrading of infrastructure components in large-scale infrastructure systems is excessively high due to the large number of variables involved, leading to significant resource allocation and time requirements for enterprises.
Innovation Solution
The method involves grouping infrastructure components into subsets based on similarity in condition, failure risk, and replacement cost, allowing for a single calculation to represent the entire subset, thereby reducing the number of variables and computational resources needed for optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual optimization is performed for each infrastructure component, then optimization precision is improved, but computational complexity increases excessively
Solution Approach 1:
The patent segments the infrastructure components into distinct groups based on their failure risk characteristics. High-risk components are separated from low-risk components, allowing different optimization strategies to be applied to each segment. This segmentation reduces computational complexity by enabling batch processing of components with similar risk profiles rather than individual optimization of each component.
Solution Approach 2:
The patent merges components with similar failure risk characteristics into unified groups. By combining multiple components that share similar risk profiles into single optimization units, the system reduces the total number of optimization calculations required while maintaining adequate precision for each component type within the group.
2Reliability
If comprehensive optimization is performed for all infrastructure components, then maintenance reliability is improved, but time consumption increases significantly
Solution Approach 1:
The patent applies different optimization approaches to different groups of components based on their specific risk characteristics. High-risk components receive intensive optimization and monitoring, while low-risk components receive simplified maintenance strategies. This local quality approach ensures maintenance reliability is optimized where needed without wasting time on components that pose minimal risk.
Solution Approach 2:
The patent applies partial optimization to low-risk components, using simplified maintenance strategies rather than comprehensive optimization. This partial action approach maintains adequate reliability for low-risk components while significantly reducing the total time required for optimization compared to applying comprehensive optimization to all components uniformly.
3Measurement precision
If detailed analysis is performed for each component, then prediction accuracy is improved, but resource allocation increases excessively
Solution Approach 1:
The patent segments components into risk-based groups, allowing detailed prediction analysis to be applied only to high-risk components where accuracy is most critical. Low-risk components are analyzed using simplified methods, reducing the total computational resources required while maintaining high prediction accuracy for the most important components.
Solution Approach 2:
The patent uses representative samples or proxy indicators to represent entire groups of components with similar characteristics. Instead of analyzing every individual component in detail, the system uses representative data points or aggregated statistics that capture the essential risk characteristics of each component group, significantly reducing resource requirements while maintaining prediction accuracy.
Data Source
AI summary
Apparatus and methods for managing and upgrading components in an infrastructure are provided. Records relating to components may be retrieved from a database and separated into subsets based on conditions of those components, the types of those components, deterioration models of those components, and/or other factors. Future condition values may be calculated for each subset, and likelihoods of failure of components may be determined for each subset based on those future condition values. The estimated condition values may be used in order to determine an optimal approach to maintaining, refurbishing, and/or replacing the components in the infrastructure. Computational efficiency is increased.