Two-Stage Compressive Sensing for NDE Data Reduction
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Solution Overview
Problem
Current structural health monitoring (SHM) technologies face challenges in efficiently inspecting large and complex structures due to difficulties with large sensor arrays, timely analysis of large data sets, and weight concerns, which hinder the adoption of reliable damage detection and characterization methods, especially in inaccessible environments like space or orbit.
Innovation Solution
A generalized Compressive Sensing methodology is developed for automated reduction of NDE/SHM data, utilizing two-stage data acquisition and analysis to undersample sensor signals from both time and space, reducing data acquisition and processing burdens while maintaining damage detection accuracy, and incorporating a graphical user interface for data reconstruction and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional NDE inspection methods are used on large complex structures, then damage detection capability is maintained, but data acquisition time and data set size increase significantly
Solution Approach 1:
The patent applies compressive sensing to acquire only a partial subset of sensor data (e.g., 10-30% of full sampling) while using mathematical reconstruction algorithms to recover the complete information. This partial sampling approach reduces data acquisition time and volume while maintaining damage detection capability through intelligent signal processing that exploits the sparse nature of damage signatures in the data.
2Measurement precision
If full sensor arrays are deployed for comprehensive monitoring, then damage detection accuracy is improved, but system weight and complexity increase
Solution Approach 1:
The patent extracts and removes redundant sensors from the monitoring system, keeping only a sparse subset of sensors (e.g., 10-30% of the full array). Compressive sensing reconstruction algorithms then mathematically recover the information that would have been provided by the removed sensors, thereby reducing system weight and complexity while maintaining damage detection accuracy.
3Reliability
If comprehensive sensor data is collected for accurate damage characterization, then detection reliability is improved, but data processing burden and storage requirements increase
Solution Approach 1:
The patent extracts and removes redundant data from the acquired sensor signals by applying compressive sensing reconstruction. Instead of processing complete high-volume data sets, the system processes only the essential information needed for damage detection, thereby reducing computational burden and storage requirements while maintaining detection reliability through mathematically guaranteed signal recovery.
Data Source
AI summary
The disclosure deals with a system and method for Compressive Sensing, which has been shown to greatly reduce data acquisition and processing burdens by providing mathematical guarantees for accurate signal recovery from far fewer samples than conventionally needed. A generalized Compressive Sensing methodology developed for automated reduction of non-destructive evaluation/structural health monitoring (NDE/SHM) data is effective for multiple types of NDE/SHM systems. The methodology uses Compressive Sensing at two stages in the data acquisition and analysis process to detect damage. The two stages are: (1) temporally undersampled sensor signals from (2) spatially undersampled sensor arrays, resulting in faster data acquisition and reduced data sets without any loss in damage detection ability. In addition, a graphical user interface helps guide and visualize the associated data reconstruction process.