Asset Integrity Engine for Dynamic Reliability Forecasting
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Solution Overview
Problem
Selecting the most appropriate assets for operations and predicting their future performance is complex, especially for large groups and dynamic criteria, leading to uncertainty and potential errors.
Innovation Solution
A method and system for forecasting asset reliability by identifying peer units, constructing local predictive models, and dynamically updating them based on changing criteria, using an asset integrity engine that employs evolutionary algorithms to define similarity and relevance criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional asset selection methods are used for large groups with dynamic criteria, then the selection process becomes complex and error-prone, but switching to advanced forecasting methods increases system complexity and computational requirements
Solution Approach 1:
The patent segments the asset selection process into distinct components: identifying peer units based on similarity criteria, constructing local predictive models for each asset, and dynamically updating these models as criteria change. This segmentation transforms the complex overall problem into manageable discrete steps, improving selection accuracy without overwhelming system complexity
Solution Approach 2:
The system dynamically changes parameters by updating similarity criteria and model parameters as operational conditions evolve. This allows the forecasting system to adapt to dynamic criteria automatically, maintaining high selection accuracy while the parameter updates are handled through structured algorithms rather than ad-hoc complexity
2Measurement precision
If detailed individual asset information is collected and analyzed, then selection accuracy improves, but the time and computational resources required increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-identifying peer units and constructing local predictive models before actual asset selection is needed. Historical data is analyzed in advance to build these models, so when selection is required, the system can quickly query and compare pre-computed predictions rather than performing full analysis in real-time
Solution Approach 2:
The patent uses copying by creating local predictive models that replicate the behavior patterns of peer units. Instead of analyzing all asset data from scratch for each selection decision, the system copies relevant historical patterns into localized models that can be quickly queried, dramatically reducing selection time while maintaining accuracy
3Adaptability or versatility
If static selection criteria are used, then the selection process is simpler, but it cannot adapt to dynamically changing operational conditions and asset performance patterns
Solution Approach 1:
The system implements dynamics by making the selection criteria and predictive models adaptive rather than static. As new operational data becomes available and conditions change, the system dynamically updates the local predictive models and similarity criteria. This adaptability is achieved through structured update mechanisms that automatically incorporate new information without requiring complete re-analysis
Solution Approach 2:
The patent incorporates feedback loops where the performance of assets and the accuracy of predictions are continuously monitored. This feedback informs updates to the local predictive models and similarity criteria, enabling the system to adapt to changing conditions. The feedback mechanism follows structured algorithms that update models based on prediction errors and new data, balancing adaptability with controlled complexity
Data Source
AI summary
A method and system of forecasting reliability of an asset is provided. The method includes identifying peer units of the asset by using selected criteria, performing a search for the peer units based upon the selected criteria, and constructing local predictive models using the peer units. The method also includes estimating the future behavior of the asset based upon the local predictive models and dynamically updating the local predictive models to reflect at least one change in the criteria.