Correlation Management System for Asset Attribute Influence Ranking
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Solution Overview
Problem
The challenge lies in determining the influence of various attributes on asset performance, as the extent of an attribute's impact may be obscured by other influencing factors, especially in complex systems with increasing connectivity and data availability.
Innovation Solution
A correlation management system is employed to identify correlations between asset attributes using different correlation models based on data-type combinations, selecting the most influential attributes and configuring them to maintain preferred values, thereby improving asset performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple correlation models are used to determine attribute correlations, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the correlation analysis task by dividing attributes into different data-type categories (continuous, discrete, categorical) and applying specific correlation models to each category. This segmentation allows the system to use multiple specialized models without overwhelming complexity, as each model is tailored to specific data types rather than using a single general-purpose model for all attributes.
Solution Approach 2:
The patent changes the parameter of correlation model selection based on the data-type parameters of the attributes being analyzed. By dynamically selecting appropriate correlation models (e.g., Pearson for continuous-continuous, chi-square for categorical-categorical) based on attribute data types, the system achieves high measurement precision while maintaining manageable complexity through parameter-based model selection.
2Loss of information
If correlation analysis is performed on all attribute pairs, then information completeness is improved, but loss of time increases
Solution Approach 1:
The patent extracts and identifies only the most influential attributes by applying correlation models selectively to determine which attributes have significant correlations with the target attribute. Rather than treating all attribute pairs equally, the system extracts the key influential relationships, reducing computation time while preserving the most important information about attribute influences.
Solution Approach 2:
The patent applies different levels of analysis depth to different attribute pairs based on their local characteristics. By using data-type-specific correlation models and focusing computational resources on attribute pairs with potential significant relationships, the system achieves comprehensive information gathering without uniformly high computational cost across all pairs.
3Loss of information
If data from multiple sources is integrated, then information completeness is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent creates a universal correlation analysis framework that handles multiple data types (continuous, discrete, categorical) from various sources through a unified approach. The system uses a common architecture that selects appropriate correlation models based on data-type parameters, enabling integration of diverse data sources without requiring separate analysis pipelines for each data type, thus reducing detection and measurement complexity.
Data Source
AI summary
Operations associated with determining correlations between various attributes are disclosed. The operations may include: identifying a target attribute and a plurality of influencing attributes, determining a first correlation value representing a first correlation between the target attribute and a first influencing attribute of the plurality of influencing attributes, determining a second correlation value representing a second correlation between the target attribute and a second influencing attribute of the plurality of attributes, and based on the first correlation value and the second correlation value, ranking the first influencing attribute higher than the second influencing attribute in a ranked list of the plurality of influencing attributes representing an influence of each of the plurality of influencing attributes on the target attribute.