Equipment Failure Data Classification System
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Solution Overview
Problem
In oil and gas exploitation, sucker-rod pumping systems face numerous types of failures, making it challenging to effectively detect and classify equipment failures in real-time, which hampers efficient operation and maintenance.
Innovation Solution
A system comprising a processor that executes computer-executable components to group oil and gas exploration equipment failure data into failure type groups based on determined criteria, using similarity algorithms and expert consensus data to facilitate identification and classification of equipment failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If equipment failure data is manually analyzed and classified, then classification accuracy can be maintained, but time consumption and operational complexity increase significantly
Solution Approach 1:
The patent replaces manual mechanical analysis with automated computer-executable components that process equipment failure data. The system uses algorithms to automatically group and classify failure data, substituting human operators with computational systems to reduce time consumption while maintaining classification accuracy through systematic processing.
Solution Approach 2:
The patent creates structured representations of failure data through grouping components that organize raw data into standardized formats. By copying and structuring data into consistent groups, the system enables efficient automated processing while preserving the essential characteristics needed for accurate classification.
2Adaptability or versatility
If multiple failure types are monitored simultaneously, then comprehensive detection capability is improved, but system complexity and difficulty of detection increase
Solution Approach 1:
The patent segments the complex task of monitoring multiple failure types into distinct grouping components, each handling specific failure categories. By dividing the monitoring system into specialized modules that process different failure types separately, the system achieves comprehensive detection capability while reducing overall complexity through modular organization.
Solution Approach 2:
The patent introduces grouping components as intermediary structures between raw failure data and final classification. These intermediaries organize and structure diverse failure data into standardized groups, making the detection process more manageable and reducing the complexity of analyzing multiple failure types simultaneously.
3Measurement precision
If equipment failure data is grouped into detailed categories, then identification precision is improved, but data processing complexity increases
Solution Approach 1:
The patent segments failure data into hierarchical groups ranging from broad categories to specific failure types. This segmentation allows the system to achieve detailed identification precision by progressively narrowing down from general to specific categories, while managing processing complexity through the structured hierarchical approach.
Solution Approach 2:
The patent adds a categorical dimension to failure data organization by grouping data along multiple classification axes. This dimensional approach enables precise identification through multi-criteria classification while managing complexity by organizing data in structured multidimensional groups rather than flat structures.
Data Source
AI summary
The subject disclosure relates to employing grouping and selection components to facilitate a grouping of failure data associated with oil and gas exploration equipment into one or more equipment failure type groups. In an example, a method comprises grouping, by a system operatively coupled to a processor, training data of a set of equipment failure data into one or more failure type groups based on one or more determined failure criteria, wherein the one or more failure type groups represent equipment failure classifications associated with energy exploration processes; and selecting, by the system, first ungrouped data from the set of equipment failure data based on a level of similarity between the first ungrouped data and the training data.