Anomaly Detection via Principal Eigenvector Angle Changes
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Solution Overview
Problem
Existing technologies fail to timely detect and mitigate anomalies and degradations in physical devices, leading to performance issues, potential hazards, and increased remediation costs.
Innovation Solution
A system that processes streaming data from sensors to determine principal eigenvectors and calculate angle changes between data windows, detecting anomalies and degradations by exceeding predefined thresholds, and generates electronic signals for mitigating actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used to detect device failures, then detection capability is limited, but processing time and resource consumption increase
Solution Approach 1:
The patent transforms sensor data into a different parameter space by computing principal eigenvectors and their angular relationships. This parameter transformation enables more efficient anomaly detection by comparing angle changes between consecutive data windows, reducing processing complexity while improving detection precision.
Solution Approach 2:
The patent replaces traditional mechanical failure detection methods with a computational approach using linear algebra operations. By substituting direct physical monitoring with mathematical transformations of sensor data, the system achieves faster processing and lower resource consumption while maintaining high detection accuracy.
2Reliability
If comprehensive sensor monitoring is implemented, then detection accuracy improves, but device complexity and resource consumption increase
Solution Approach 1:
The patent extracts only the essential information from comprehensive sensor data by computing principal eigenvectors that capture the dominant patterns. This extraction approach maintains high detection reliability by focusing on the most significant data characteristics while discarding redundant information, thereby reducing processing complexity.
Solution Approach 2:
The patent segments the monitoring task into distinct computational steps: data windowing, principal eigenvector computation, angle calculation, and threshold comparison. This segmentation allows each step to be optimized independently and enables parallel processing, reducing overall system complexity while maintaining comprehensive monitoring capability.
3Speed
If real-time anomaly detection is implemented, then response time improves, but computational resource consumption increases
Solution Approach 1:
The patent applies partial action by computing only the principal eigenvector (the most significant component) rather than performing complete eigen decomposition. This partial computation approach enables real-time anomaly detection by focusing on the dominant patterns in the data, significantly reducing computational energy consumption while maintaining detection speed.
Data Source
AI summary
Physical-device anomalies and degradation can be mitigated by implementing some aspects described herein. For example, a system can determine a first data window and a second data window by applying a window function to streaming data. The system can determine a first principal eigenvector of the first data window and a first principal eigenvector of the second data window. The system can determine an angle change between the first principal eigenvectors of the two data windows. The system can then detect an anomaly based on determining that the angle change exceeds a predefined angle-change threshold. Additionally or alternatively, the system may compare the first principal eigenvector for the second data window to a baseline value to determine an absolute angle associated with the second data window. The system can then detect a degradation based on determining that the absolute angle exceeds a predefined absolute-angle threshold.