Outlier Causality Analysis for Composite Test Data Retesting
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Solution Overview
Problem
Current outlier detection techniques in aerospace and automotive industries face challenges in accurately identifying and managing outliers in test data from composite parts, leading to inaccurate material property characterization and compliance issues due to noise and equipment errors.
Innovation Solution
A computer system analyzes test data using multiple outlier detection methods to identify outliers, determines their causality, and retests the physical structure with identified changes, generating new data to improve accuracy and compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple outlier detection methods are used to analyze test data, then the accuracy of outlier identification is improved, but the complexity of the detection system increases
Solution Approach 1:
The patent segments the outlier detection process into multiple independent detection methods (e.g., statistical methods, machine learning methods, domain-specific methods) that analyze different aspects of the test data. Each method operates independently and produces separate results, which are then integrated to form a comprehensive outlier identification. This segmentation allows the system to achieve high accuracy through multiple perspectives while maintaining modularity that manages complexity.
Solution Approach 2:
The patent merges the results from multiple independent outlier detection methods by integrating their outputs through a unified framework. This merging process combines the strengths of different detection approaches (statistical, machine learning, domain-specific) to produce a more accurate and reliable outlier identification than any single method could achieve alone, while the unified framework manages the overall system complexity.
2Reliability
If outliers are thoroughly analyzed and causality is determined, then the quality of test data is improved, but the time required for data processing increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing test data to prepare it for outlier detection, pre-defining detection thresholds and parameters based on historical data and domain knowledge, and pre-identifying potential outlier patterns. This preliminary preparation reduces the computational burden during actual outlier analysis, enabling thorough causality determination while minimizing processing time during critical evaluation phases.
Solution Approach 2:
The patent implements feedback mechanisms where the results of outlier detection and causality analysis are fed back into the system to refine detection parameters, update domain knowledge bases, and improve future outlier identification. This feedback loop enables the system to learn from past analyses, becoming more efficient over time while maintaining high data quality standards through continuous refinement of detection algorithms and parameters.
3Reliability
If retesting is performed with identified changes, then compliance with regulations is ensured, but the overall testing duration increases
Solution Approach 1:
The patent performs preliminary actions by identifying and implementing changes based on outlier causality analysis before formal retesting is required. By proactively addressing identified issues through targeted modifications and preliminary validation, the system ensures regulatory compliance is achieved earlier in the process, reducing the need for extensive retesting and thereby shortening the overall testing duration while maintaining compliance standards.
Data Source
AI summary
A method, apparatus, system, and computer program products for managing a set of outliers in test data. A computer system analyzes a set of features derived from the test data using different outlier detection methods to generate a result of the set of outliers identified by the different outlier detection methods. The test data is obtained from testing a physical structure. The computer system determines a causality for the set of outliers in the result. The physical structure is retested with a set of changes determined using the causality identified for the set of outliers. The retesting generates new test data for the physical structure.