Feature Clustering and Redundancy Removal for Faster Fault Analysis
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Solution Overview
Problem
Complex systems like aerospace propulsion and gas turbine engines generate vast amounts of data, making it time-consuming for engineers and data scientists to identify issues and perform corrective actions, leading to system downtime and associated costs.
Innovation Solution
A computer-implemented method that receives a dataset, identifies non-redundant and redundant features, clusters them, and allows user input for feature removal and retention, determining feature importance using multiple criteria and performing actions like predictive modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If engineers and data scientists manually review vast quantities of data generated by complex systems, then they can identify problems and perform corrective actions, but the process becomes time-consuming and results in system downtime
Solution Approach 1:
The patent extracts and removes redundant features from the dataset, keeping only the most informative features for analysis. This reduces the data volume that engineers need to review while preserving the critical information needed for problem identification, thereby reducing analysis time without sacrificing accuracy
Solution Approach 2:
The patent creates a simplified representation of the original data by generating a reduced feature set that captures the essential information. This copied representation can be analyzed quickly while maintaining the ability to identify system problems accurately
2Reliability
If a large team of engineers and data scientists is deployed to review system data, then comprehensive analysis can be performed, but the process becomes inefficient and costly
Solution Approach 1:
The patent extracts the most critical features from the dataset using automated algorithms, eliminating the need for large teams to manually review all data. This maintains analysis comprehensiveness by focusing on the most informative features while dramatically improving efficiency
Solution Approach 2:
The system performs self-service by automatically identifying and removing redundant features, and by autonomously determining which features are most informative for problem detection. This automated feature selection replaces manual team review while maintaining or improving analysis quality
3Reliability
If all features from the data set are analyzed, then complete system monitoring is achieved, but the complexity of data processing increases
Solution Approach 1:
The patent automatically extracts and removes redundant features from the dataset, keeping only the essential features for analysis. This reduces data processing complexity while maintaining complete system monitoring coverage by focusing on the most informative features
Solution Approach 2:
The patent changes the parameter set by transforming the original feature space into a reduced feature space. This parameter transformation maintains the information needed for complete system monitoring while simplifying the data structure and reducing processing complexity
Data Source
AI summary
A computer-implemented method including: receiving a first data set including a plurality of values for a plurality of features; identifying at least a first feature of the first data set that is non-redundant and at least a second feature of the first data set that is redundant; identifying one or more clusters of features in the plurality of features of the first data set, a first cluster of the one or more clusters including at least the first feature and the second feature; and controlling a display to display the first feature and one or more redundant features from the first cluster, the displayed one or more redundant features from the first cluster including the second feature.